Towards intelligent electric vehicle power batteries and multi-scenario application vehicle operation safety charging strategies: a review
Shan
Li
,
Jian
Ma
,
Xuan
Zhao
*,
Kai
Zhang
*,
Zhipeng
Jiao
and
Qifan
Xue
School of Automobile, Chang'an University, Xi'an 710064, China
First published on 20th June 2024
Abstract
New energy vehicles encounter problems such as short mileage and restricted use environments throughout their development and commercialization, and the service life of lithium-ion batteries, as the main development direction of power batteries, is affected by charging strategies and charging environments. Accordingly, the study of charging strategies for lithium-ion batteries is crucial for the future development of intelligent battery management systems and new energy vehicles. Herein, we introduce in detail the charging methods and characteristics of different charging strategies and their equalization control technologies based on battery cells and modules and present an overview of the charging mode of the whole vehicle. Finally, we summarize the shortcomings of the current research, propose a specific process for optimizing the intelligent charging strategy of electric vehicle power batteries, look forward to its development prospects, and highlight the future research direction and focus.
1. Introduction
Since electric vehicles are becoming increasingly popular, their battery charging time is now excessively long and its low range is progressively attracting attention. In this case, a substantial increase in the charging current can improve the charging speed. However, excessive current charging will erode the capacity of the battery. According to research, the best operating temperature range for lithium-ion batteries is 20–45 °C. High-current charging generates considerable heat, and when the battery temperature increases above a certain point, the thermal runaway reaction takes place, which can result in dangerous mishaps.1,2 In addition to triggering battery safety issues, high-current charging can also lead to battery health degradation issues, mainly triggering a side reaction of lithium precipitation from the negative electrode.3,4 Lithium ions are primarily inserted into the positive and negative electrodes to charge lithium-ion batteries, and a reasonable choice of charging control strategy can reduce battery polarization, maintain the dynamic balance of charging within the battery, prevent the phenomenon of overcharging, and ensure the safe use of the battery.5–7 At present, for a lithium-ion single-cell battery, the commonly used charging strategies are constant current–constant voltage (CC–CV) charging, pulse charging, multi-stage charging, and model-based charging, and many researchers and scholars have fully investigated the advantages, disadvantages, and scope of the application of different charging strategies. To solve the range anxiety phenomenon commonly found among electric vehicle (EV) owners, meet the needs for different charging performances of batteries, and enhance the overall charging efficiency of batteries, various charging methods must be used based on various charging requirements. Long charging queuing times for EVs, low utilization rates of charging piles, difficulty for charge-point operators to make profits, and other related problems constrain the promotion and application of EVs.8 Thus, it is necessary to remove the limitation of lithium-ion power battery charging technique, formulate a unified specification and meet the range requirements of EVs. Scholars have reviewed different charging strategies, and ref. 9 studied the characteristics and applications of different optimized charging strategies and pointed out that based on electrochemical or thermal models, better charging results can be obtained by starting with the internal charging mechanism of the battery. Ref. 10 examined in depth the impact of various charging multipliers and different numbers of stages. Ref. 11 reviewed the algorithms for constructing fast charging strategies and discussed the advantages and disadvantages of the algorithms and the applicable models. Ref. 12 studied the influence of battery electrode materials, electrolyte types, battery, and battery pack design on power battery fast charge optimization technology. Ref. 13 analyzed the charging types and charging methods of electric vehicles in Spain, providing theoretical support for policy formulation. Based on 11 months charging data for a large electric vehicle, ref. 14 analyzed when, where, and how users charged their vehicle and quantitatively examined the connection between electric car energy use and charging techniques.However, the power battery module and charging method of electric vehicles have not been examined in the literature to date. The research on EV lithium-ion power battery charging strategies has been performed based on four perspectives including CC–CV, pulse, multi-stage, and model-based strategies. The lithium-ion power battery charging procedures are summarized under the four different charging strategies, as shown in Fig. 1. Herein, we provide a systematic review of lithium-ion single-cell batteries, power pack charging strategies, and vehicle charging modes. Alternating current (AC) slow charging, directing current (DC) fast charging, wireless charging, and quick battery replacement are the many types of car charging modes. The most important events in the field of intelligent electric vehicle power batteries and multi-scenario application vehicle operation safety charging strategies are illustrated in Fig. 2. This is very helpful for readers to gain insight into the most important events in this field. In addition, we combine the current research hotspots and deficiencies in the future development of this field to provide innovative research ideas and breakthrough design methods for the development of smart and safe charging strategies for electric vehicles.
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| Fig. 1 Summary of the charging methods in different charging strategies. | ||
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| Fig. 2 The most important events in the field of intelligent electric vehicle power batteries and multi-scenario application vehicle operation safety charging strategies. | ||
2. Lithium-ion power single-cell battery charging strategy
Due to their superior performance, lithium-ion batteries have replaced other types of power batteries in electric vehicles, and accordingly their safety should be ensured while pursuing fast charging during the charging process. Many academics both domestically and internationally have devoted their efforts to research on charging lithium-ion batteries and proposed various charging strategies to establish a theoretical framework and technical assistance for lithium-ion batteries to address the concerns associated with charging electric vehicles. Presently, the most common charging techniques for lithium-ion power single-cell batteries include pulse charging, multi-stage charging, CC–CV charging, and model-based charging. The benefits and drawbacks of these charging strategies, which often only have one or two charging parameters, are explored in this study. These charging strategies are straightforward and dependable.2.1 Constant current–constant voltage charging strategy
The most common charging method is CC–CV. As shown in Fig. 3, initially to charge the battery, a fixed, predetermined multiplier current is employed. Subsequently, it becomes constant-voltage (CV) charging when the voltage hits the charging cut-off voltage.15 At this point, the charging voltage of the battery serves as the cut-off voltage. This method of charging overcomes the drawbacks of constant-current (CC) charging, avoiding damage to the battery and enabling it to charge to its maximum capacity by starting CV charging at a high current rate. As demonstrated in Fig. 4, the numerous control settings of the CC–CV charging strategy greatly affect the charging time, capacity, effectiveness, temperature, life, and other aspects of the battery. | ||
| Fig. 3 CC–CV charging curve. | ||
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| Fig. 4 Influencing parameters in the CC–CV charging strategy. | ||
Based on CC–CV charging methodologies, many researchers and academics have examined the effect of the battery capacity on its charging cut-off voltage, charging time, and life. Asakura et al.16 performed a series of life experiments to examine how well lithium-ion batteries held up over time at various temperatures and charging cut-off voltages. The results revealed that a rise in charging cut-off voltage at high temperatures greatly accelerates the battery degradation process. Dong et al. carried out experimental studies to examine how the performance of a battery is affected by its charging cut-off voltage,17 and their findings are as follows: lowering the 4.2 V charging cut-off voltage to 4.15 V can quicken the charging of lithium-ion batteries without significantly reducing their charging capacity or other fundamental properties. The charging cut-off voltage was increased from 4.2 V to 4.9 V due to the need to evaluate how high-voltage charging affects batteries, according to Maher et al.18 The anode and cathode crystal structures of the battery deteriorated due to the high cut-off voltage, according to the analysis of the thermodynamic data.
Research has also focused on the effect of various charging multiplicities on batteries, demonstrating that enhancing the multiplicity of the charging current during the CC stage can greatly accelerate charging. The authors19 investigated the overcharging behavior of batteries with different charging currents at different multiplicities, where low-multiplicity charging resulted in the decomposition of the internal electrolyte, while high-multiplicity charging caused the accumulation of internal heat, which ultimately resulted in the melting of the diaphragm. Also, it has been found that the charging rate of ternary soft pack lithium-ion batteries has a linear relationship with the maximum temperature and peak voltage at which thermal runaway occurs.20 Ref. 21 carried out overcharge life simulation experiments on Li–FePO4 battery modules with different charging multiples, and the results showed that a lower rate current overcharge resulted in the phenomenon of gas production, which is less hazardous, while a higher rate current caused the battery module to experience thermal runaway, which will lead to serious consequences. Zhou22 discovered that by examining how various charging multiplicities affect the battery cathode material, the charging multiplicities increase, and subsequently the aging of the cathode material increases. Meanwhile, at a certain charging diversity, the greater the number of charging cycles, there is a decline in the content of the cathode active material of the battery.
The temperature and internal resistance of a battery are closely related, together with its charging time and charging current magnitude in CC–CV strategies. Du et al. revealed the aging cycle performance of lithium-ion batteries across a range of temperatures and charging multiplicities, including at lower temperatures, where the capacity of the lithium batteries decreased at a more pronounced rate compared to high temperatures and their performance declined more severely when they were operated in temperature intervals that were greater than the safety range. Specifically, low temperatures primarily damaged the anode, whereas high temperatures mainly harmed the cathode, according to this study.23 Koleti et al.24 found that Li-ion batteries exhibit high and low-temperature operating characteristics with changes in temperature. The lower the internal resistance, the less power the battery needs to produce a current. By raising lithium-ion battery operating temperatures within a specific range, the ion diffusion rate inside the battery also increases. Yang et al.25 utilized the CC–CV charging approach, where the temperature was increased to 60 °C and the battery charged to eliminate lithium plating. Employing this method, the capacity was still 91.7% after charging a 209 W h kg−1 battery for 2500 cycles in 10 min, which is higher than the target of the U. S. Department of Energy, as shown in Fig. 5. Inoa et al.26 used the Gaussian pseudo-spectral method to reduce the energy consumption during charging. Utilizing CC–CV charging methodologies, we were could optimize the energy usage and temperature increase, respectively, to derive the best charging curves. Liu et al.27 provided a charging approach with three optimization objectives based on charging time, charging temperature rise, and charging energy loss. The ideal weights were determined through the investigation of various optimization techniques and trade-off considerations. The charging current curves with various priorities were derived using the suggested optimal charging technique for the battery. According to Zhang et al., limitation bounds in the charging current curve were formed with an increase in the polarization voltage and temperature, and they determined that the limiting factor for the charging current is temperature rise, while that in the late-charging stage is polarization voltage. Also, the optimal charging strategy with a shorter time and larger capacity was proposed based on the current curve.28 Tippmann et al.29 used a 1D + 1D (pseudo-2D) electrochemical model coupled with a thermal model, and the electrochemical impedance spectra parameterized the model with the measured data in the frequency domain to compare the decay rates under CC–CV charging conditions at various temperatures and charging currents, and the findings can be used to guide the creation of CC–CV charging protocols at low temperatures.
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| Fig. 5 Charging preheating method.26 | ||
Additionally, when maximizing the CC–CV technique, the length of the charging time has been found to significantly affect the charging process by a few researchers. Abdollahi30 hypothesized that the weighted total of the energy loss and charging time should be minimized to improve the CC–CV charging approach and find an analytical solution for the ideal CC stage current. Similarly, according to Hu,31 optimizing battery charging necessitates careful consideration of both the energy consumption and charging duration, and Hu determined the optimal solution using numbers.
Throughout the lifespan of lithium-ion batteries, lithium precipitation side reactions can negatively impact the capacity, energy, and energy efficiency of the battery.32 Lithium precipitation is associated with the operating temperature, charging current multiplication, overcharging, and other factors of the battery, and considering the life, safety, and charging rate of the battery under the premise of inhibiting lithium precipitation, a high current charging is used. Therefore, it is necessary to explore the suitability of the battery charging current under different operating conditions, i.e., the maximum charging current without lithium precipitation at different temperatures, different charging multipliers, and different SOCs. Sun et al. investigated the law of change of low temperature and charge multiplication in the generation of lithium plating in lithium batteries. A decrease in temperature and increase in charging multiplicity will increase the generation of lithium plating; however, this law does not apply at −20–0.5 °C, and thus it is believed that lithium plating exists in a certain interval. Accordingly, the use of the lithium plating Coulomb efficiency has been proposed to evaluate its reversibility.33 Yue et al. found that the anode could achieve stable cycling when the lithium plating amount reached 40% of the capacity. A 1.2 A h pouch battery was used, which could be cycled for more than 150 times at an ambient temperature of 6 °C with a capacity retention rate of 84.4%, verifying the high reversibility of lithium plating.34
In addition, some scholars focused on how to characterize the lithium plating and quantitatively detect the lithium plating process, which can make the battery last longer and charge more efficiently. Liu et al. classified the detection of lithium plating behavior under fast-charging conditions employing ex situ and in situ techniques, where in the case of the ex situ techniques, the graphite electrodes were characterized using various types of microscopes by dismantling the battery. In contrast, the in situ techniques could real-time monitor the operating status and the generation and change pattern of lithium plating in the battery. The generation of lithium plating and its change law, differential open circuit voltage, electrochemical impedance spectroscopy, and differential voltage are some of the most popular approaches.35 Among them, Xu et al. suggested that the relaxation time constant can serve as a quantitative indication for monitoring lithium plating in lithium-ion batteries, and they conducted the appropriate tests on a half-cell and full-cell and verified the validity of the indicator at different temperatures and charging rates. They found that although the relaxation time method has potential and advantages in detecting lithium plating, it still has certain errors in practical operation, and the algorithm needs to be optimized to improve its detection accuracy in further work.36 In the next research work, Xu et al. further proposed the use of operando lithium plating to determine lithium plating based on dynamic capacitance measurement tests, revealing that 15 Hz is the characteristic frequency of the graphite anode during charge transfer and that the use of a suitable frequency can eliminate the influence of the cathode characteristics on the battery.37 Konz et al. proposed that the use of voltage relaxation profile derivatives (DOCV) can be the same as the detection of the lithium-plating start time, where its profile is consistent when not lithium-plated to avoid misjudging lithium-plating. Alternatively, the DOCV curve varies greatly between battery cells. The design of the screening test in the 11th 50% charging cycle can show the lithium plating characteristics, and then before the differences caused by the differences between the battery or 50% of the SOC cycle due to the battery electrode aging cycle error.38 Li et al. proposed a parameter-independent rare-earth measurement error correction method for reference electrode measurements of polarization voltage without considering the electrochemical and geometrical properties of the battery, which can propose the critical charging rate for lithium plating in a safe range and realize the detection of lithium plating in the fast-charging process of lithium batteries.39 S. S. Zhang et al.40 demonstrated that the phenomenon of lithium plating takes place during charging processes characterized by high currents and low temperatures. When the current exceeds 0.4 C, although it will not dramatically reduce the charging time, boosting the current will make the lithium plating worse, which will prolong the charging duration of the CV and increase the lithium plating, and thus it is necessary to reduce the battery temperature. In addition, it has also been found that the use of the CC–CV charging strategy at low temperatures will extend the charging time and induce the lithium precipitation phenomenon. A spherical diffusion model-based CC–CV charging approach was proposed by Li et al. Specifically, a larger charging current and low lithium-ion diffusion coefficient during the CC stage will cause electrochemical polarization, resulting in the loss of capacity in the battery; consequently, reducing the particle size can minimize the capacity loss while charging, increase the diffusion coefficient, and lower the charging multiplicity.41 The thickness of the electrode is also large, which is not conducive to the charging process, and the excessive negative electrode active material can reduce the risk of lithium precipitation, and a larger lithium ion diffusion coefficient, together with more compact active ingredient particles contributes to enhancing the charging effectiveness.42
The anode, cathode, the fast-charging capability of lithium batteries are significantly affected by the electrolyte components and the materials used to construct batteries themselves. In recent years, some scholars have focused on this issue, studying the effects of the anode and cathode material properties on lithium plating and designing new electrolytes to improve the charging rate according to the electrolyte properties. In the previous research by the Goel team, based on their model and experimental study, the highly ordered laser-patterned electrode (HOLE) structure was formed in the original graphite anode, and fast charging was carried out at 4–6 C without lithium plating, which greatly improved the charging rate. Based on this, the optimal channel spacing of HOLE was calculated based on the continuum scale model, which further enhanced the initial capacity of the graphite anode for rapid charging.43 Hu et al. investigated the change rule of cathode materials of lithium-ion batteries under 1000 cycles of fast-charging at 6 C and the lithium-ion diffusion coefficients of the cathode materials all showed a decreasing phenomenon, which further led to an increase in polarization.44 Yao et al. used a non-polar but salt-dissolved solvent to generate a weakly solvated electrolyte, which showed a specialized solvent structure when the salt concentration was 1 M. The anion-derived SEI was more stable and flexible in terms of interfacial transport, which led to the fast-charging performance and long-life characteristics of graphite electrodes.45 Cai et al. found that new electrolytes that exhibit enhanced ionic conductivity, large transfer number, and broad temperature band can be prepared using functional additives or new lithium salts and solvents. The electrolyte has a significant impact on the electrochemical properties of the battery and the ion transport interface, and the improvement in multi-channel diffusion, fast lithium-ion insertion, and short diffusion distance for graphite-based materials can achieve fast charging.46 Yao et al. proposed that the charge transfer situation at the anode-electrolyte interface is an important factor affecting lithium plating and it is crucial to maintain the balance of the two reactions interacting with each other. Based on this, the concept electrolyte was designed to achieve charging to more than 80% in 10 min, and the decrease in the energy density value after undergoing 500 cycles of aging was only 5.2%.47 Ma et al. showed that the lithium-ion batteries of the future that are entirely solid-state should operate in a stable voltage window of greater than 5 V. At higher charging rates, significant polarization occurs at the voltage end of the battery, and the solid-state electrolyte is subjected to sustained stress, causing its capacity to decay, and thus further research into a wide and stable voltage window is essential for fast charging.48
It is easy to use and maintain CC–CV charging, but it is unable to completely eradicate the polarization phenomena that arise during charging, which hinder the charging capacity of the battery. Increasing the charging current multiplier of the battery in the CC stage and its charging cut-off voltage are two common ways to speed up CC–CV charging, but this will reduce the charging capacity and reduce the useful life of the battery and potentially pose safety risks.
2.2 Pulse charging strategy
To guarantee that the power being supplied to the battery during charging exceeds the power being drawn from it during discharge, the current is delivered to it in pulses. The charging process is considered complete once the charging voltage reaches the cut-off voltage. Fig. 6 schematically illustrates the process of pulse charging. Initially, this method was used with lead-acid batteries to avoid the significant polarization voltage associated with them.49 An interaction occurs among the positive electrode, negative electrode, and electrolyte, and the rate at which this movement occurs is what defines how quickly the battery charges and discharges.50 The same charging method has been applied to lithium-ion batteries to more uniformly diffuse and distribute ions in the electrolyte by eliminating or decreasing the polarization voltage to optimize the lifespan of the battery, while reducing the charging duration.51,52 The lithium-ion diffusion equation in lithium-ion batteries is as follows: | (1) |
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| Fig. 6 Schematic diagram of pulse charging.49 | ||
Increasing the acceptance of the battery of the subsequent cycle pulse charging is typically employed to decrease or remove the its polarization voltage, resulting in a greater average current and shorter charging times. Pulse charging techniques primarily fall into two types, voltage pulse charging and current pulse charging, which can be separated based on the objectives of the charging process.
Optimizing different charging parameters and exploring the effectiveness and safety of current pulse charging strategies have attracted extensive attention from scholars. B. K. et al.50 noted that the pulse charging strategy suppresses the large lithium-ion concentration gradient inside the battery by introducing a short interval and compared how various charging parameters affected the efficiency of pulse charging, and the charging waveforms with a gradual lengthening pulse interval or a gradual decrease in the pulse amplitude can effectively alleviate the accumulation of lithium-ion concentration and improve the charging rate. An ideal charging method using lithium-ion complementary pulse currents was proposed by Li et al.,51 employing electrochemical impedance spectroscopy to modify the pulse frequency and decrease the AC impedance of the battery. Based on an equivalent circuit model (ECM), Fang et al.55 proposed two pulse charging strategies by varying the pulse current size and the current interval, avoiding the damage to the health of the battery by an excessive charging current, while considering the charging speed.
The two fundamental methodologies for pulse charging are positive pulse current charging and negative pulse current charging, based on which several extended pulse current modes have been developed. As shown in Fig. 7, the authors in ref. 56 investigated the impacts of various pulse current charging techniques, including positive pulse current charging strategy, negative pulse current charging strategy, alternating pulse current charging strategy, charging approach using sinusoidal ripple current, and AC technique for sinusoidal ripple current charging. Huang analyzed the effects of the current frequency, the maximum temperature rise, the discharging capacity, and the charging rate on the effects of different charging strategies. After 1000 cycle tests, the battery life, highest temperature increase, energy efficiency, and improvement compared to the standard CC charging technique were 81.6%, 60.5%, and 9.1%, respectively. This was based on the two-stage deterioration model of lithium-ion batteries.57
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| Fig. 7 (a) Positive pulse current mode. (b) Pulse constant current mode. (c) Negative pulse current mode. (d) Alternating pulse current mode. (e) Sinusoidal ripple current mode. (f) AC sinusoidal ripple current mode.56 | ||
To maximize the benefit from the current pulse charging approach on the battery, subsequent research work needs to be weighted and proportioned to multiple influencing factors. High-current charging may increase the charging rate, but it also results in a decrease in charging capacity and a larger increase in temperature.
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| Fig. 8 Charging time series of VFPCS.58 | ||
The duty variable voltage pulse charger (DVVPC) approach enhances the battery charging performance by detecting and dynamically tracking the ideal charging pulse duty cycle, as depicted in Fig. 9. Compared to CC–CV, this charging technique increases the charging speed by 14%, while increasing the charging efficiency by 1.5% in comparison to the standard pulse charging technique, which uses a 50% duty cycle.59 Ref. 60 investigated the energy efficiency of a pulsed power supply coupled to a lithium-ion battery at different pulse charging voltages, reaching a peak efficiency of 22.9% at an 8 V charging voltage (Table 1).
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| Fig. 9 Operating state diagram of DVVPC.59 | ||
| Pulse charging classification criteria | Battery type | Optimization parameters | Advantages | Ref. |
|---|---|---|---|---|
| Current pulse charging | LiFePO4 | Pulse current | The relaxation process reduces the present intensity of secondary reactions occurring in the battery, which promotes the reutilization of aging batteries and improves battery capacity retention | 4 |
| Li-ion | Pulse frequency, pulse amplitude, duty cycle | Avoids time consumption due to pulse intervals | 5 | |
| Li-ion | Relaxation period | Assists in mitigating concentration polarization, enhancing power transfer efficiency, and reducing charging duration | 6 | |
| Li-ion | Current waveform parameters | Fully charged in less than an hour | 8 | |
| Li-ion | Current pulse amplitude and width | Enhanced health protection for batteries with high computational efficiency battery management system (BMS) | 9 | |
| Li-ion | Charging frequency | After undergoing 1000 cycles, experienced a significant increase in its maximum temperature | 11 | |
| Li-ion | Duty cycle, charging frequency | Lithium-ion batteries charge 17% faster and 6% more efficiently than standard CC–CV | 12 | |
| Li-ion | Charge duty cycle | 34% reduction in charging time compared to conventional CC–CV | 13 | |
| Voltage pulse charging | Li-ion | Charging frequency | Adjusted the frequency of pulse charging for lithium-ion batteries to enhance the speed of charging | 15 |
| Li-ion | Charge duty cycle | Improved charging efficiency by approx. 3.4% compared to conventional CC–CV | 16 |
A thicker passivation film will be formed at the negative electrode of the battery at high current amplitudes and the width of this passivation layer will result in an increase in impedance. In addition, the high current pulse during the final stage of charging will cause the lithium-ions in the battery to be deposited near the poles, resulting in permanent damage to the battery. The comparison of pulse charging with CC–CV charging revealed that61 when utilizing the identical mean current value for charging, pulse charging has a higher mean quadratic current, which makes pulse charging generate more heat, quickly increasing the temperature of the battery, and pulse charging has higher requirements due to the complexity and cost of charging machines, thus the pulse charging method is not widely used in practice at present.
2.3 Multi-stage charging strategy
If a higher current is utilized for charging than in the conventional steady-state phase, the duration required for charging can be decreased. Using a high current just during the CC stage can result in rapidly exceeding the top cut-off voltage at the terminals without achieving the expected charging capacity within a short period. At this juncture, we move to the CV stage, where more charging time is required, and thus the charging time may not be reduced in terms of the overall charging stage. Accordingly, a multi-stage charging method can be used to address this issue. Once the battery reaches the cut-off stage, it is initially charged at a specified current. After each pre-determined charging current is utilized, the charging process is transferred to the next pre-determined current and repeated.The charging methods include CC charging, CV charging, and CC–CV charging, and to date several optimized charging strategies have been derived by combining them with the charging characteristics of the battery and using various optimal control methods.62–64 One of the charging methods is the multi-stage charging strategy, which has been widely researched. Modulating the current intensity throughout the charging procedure can efficiently impede battery deterioration, while decreasing the charge duration. Multi-stage CC charging incorporates two or more stages with CC, culminating in a CV stage, and generally selects a higher charging multiplier in the earlier CC stage. This charging strategy charges the battery by utilizing CC of different magnitudes to achieve the objectives of increasing the charging rate, decreasing the time for charging, and additionally boosting battery life.
Voltage-based and SOC-based multi-stage charging techniques fall into two groups. Usually, the entire charging procedure is broken down into two stages or more, and most researchers divide it into 4 to 5 stages with the help of optimization algorithms. To boost the charging rate, shorten the charging time and guarantee the health of the battery, the ideal charging method is required.
An orthogonal experimental design-based global optimization algorithm, the Taguchi method (TM), has excellent parameter combination design capability and has been widely used in multi-stage charging strategies to determine the optimal current. Liu et al.65 identified the multi-stage CC charging strategy as the most effective charging method based on TM, as depicted in Fig. 10. The battery could be charged to 40% capacity in 75 min using this method, and its cycle life was 60% longer than that of the standard CV charging strategy. This research supports the idea of a five-step CC charging procedure also using TM, which takes 130.7 min to fully charge a lithium-ion battery to 95% capacity, increases the charging cycle by 57%, and decreases the charging time by 11.2%, increasing the charging efficiency by 1.02%.66
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| Fig. 10 Five-stage charging strategy with voltage limitation as the cut-off mode.65 | ||
Ref. 67 optimized a five-stage CC charging strategy based on grey correlation analysis and Taguchi design of experiments to set five charging current levels (I1 > I2 > I3 > I4 > I5). Charging was first carried out using I1, and likewise, if the terminal voltage was nearly at the maximum cut-off voltage, it was converted to I2, and then the current values were switched sequentially. According to the experimental results, this strategy shortened the charging to 25.37 min, reduced the maximum charging temperature by 0.94 °C, and reduced the surface heat generated by charging by 6.45 J compared to the CC–CV charging method. According to TM, the authors in ref. 68 set the least permissible voltage to 2.5 V. Consequently, the charging efficiency increased by 0.6% to 0.9%. The charging efficiency was enhanced by 2.8% in comparison to the optimal charging mode, which also lowered the increase in temperature by 9.3 °C, while maintaining the same charging capacity.
Particle swarm optimization algorithms, dynamic programming optimization algorithms, genetic algorithms, whale optimization algorithms, and ant colony optimization algorithms are also often used in multi-stage charging strategies to improve the charging speeds and reduce charging times. Wang et al.69 employed a five-stage charging strategy with a stepwise decrease in current, and the upper limit for the cut-off voltage was set at 4.2 V. In a search algorithm based on an adaptive evaluator for particle swarm optimization of a multi-stage charging strategy, the battery could be fully charged to over 88% of its capacity in 51 min, and compared to the traditional CC–CV charging strategy, the minimum charging time, the longevity and charging efficiency were enhanced by approximately 56.8%, 21%, and 0.4%, respectively. Lee et al.70 searched for a charging strategy to satisfy the maximum capacity and runtime objectives in the shortest charging time utilizing the Taguchi particle swarm optimization technique. In the experiment, the optimal four-stage CC charging strategy was used. Utilizing a semi-empirical aging model, thermal model, and electrical model, Zhao et al.71 suggested a multi-stage charging approach with a whale optimization algorithm. As shown in Fig. 11, as the cut-off voltage increased, the internal temperature of the battery increased, its health degraded more quickly, and charging current, order, etc. all had an impact. The health loss of the battery was also investigated, and it was found that the proposed technique decreases the charging time by 11.45%. Liu et al.72 utilized an ant colony algorithm to obtain a five-stage charging strategy, which can fill up to 70% of the battery's charge in 30 min.
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| Fig. 11 Variation in the battery state at different cut-off voltages.71 (a) Internal temperature. (b) Health loss. | ||
When utilizing multi-stage charging strategies, many scholars have focused on issues such as fluctuations in battery temperature and battery health. Liu et al.73 proposed an optimized multi-stage CC–CV charging technique, considering the health, charging duration, and energy conversion efficiency of the battery. Also, a larger cut-off voltage was used to increase the capacity of the battery, and this charging strategy ensured healthy charging of the battery based on the equilibrium between the charging rate and energy conversion effectiveness. Li et al.74 added small segments of CV charging to form an adaptive CC–CV charging strategy based on a multi-stage CC charging strategy, commencing the charging cycle with the application of a high current to reach a voltage threshold of 4.2 V, and then gradually decreasing the current to reduce the unnecessarily extended duration is required for charging during the CV phase, which can inhibit an increase in the temperature of the battery. The charging procedure was separated into three stages by Ansean et al.,75 where the main capacity charging is realized in the beginning, and the next stage of charging is turned on once the terminal voltage reaches 3.6 V. The current is gradually reduced in the last two stages of charging, which ultimately enables a reduced charging time without severely impacting the lifespan of the battery.
Presently, increasing the battery capacity in less time is the focus of research. To extend the CC charging procedure and achieve a higher capacity, Wu et al.76 presented a multi-stage CC–CV charging method. This strategy involves reducing the voltage at the terminals when it exceeds the voltage that turns off charging. At 25 °C, the charging capabilities of the multi-stage CC–CV approach were 1.246 A h and 1.169 A h, which were measured at 0 °C and −10 °C, respectively. Concerning lithium-ion batteries, which boast a charging cut-off voltage of 3.6 V, Meng Xu et al.77 investigated a two-stage CC rapid charging method. Employing this method, the battery could be charged to 80% of its rated capacity in 30 min.
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| Fig. 12 Multi-stage charging strategy with equally spaced SOC as a cut-off condition.78 | ||
Ref. 79 proposed a new MS-CC charging strategy to swiftly identify the best charging rate combinations in a charge state of 0–80%, and the batteries were tested at 25 °C, 50 °C, and 10 °C. Lee et al.80 proposed an SOC-controlled, four-stage CC charging plan, which charged more quickly than the charging strategies based on voltage limits and CC–CV. However, compared to analogous CC–CV and pulse current charging schemes, its charging efficiency is slightly lower. Li et al.81 designed a multi-stage fast-charging strategy to extend the life of ink lithium-ion batteries by dividing the condition of charge into five stages and setting different charging currents for different SOC ranges, which resulted in extended battery life and a 20% reduction in charging time after 200 cycle tests compared to the standard charging method. Wang et al.82 proposed an SOC-based MS-CC charging technique that decreases the charging time, capacity, and temperature increase. The fixed weight factor strategy could increase the charging temperature by 1.56 °C at 0 °C compared to the variable weight factor strategy and lower it by 3.45 °C at 25 °C, making every effort to maintain the charging temperature within the optimal range (Table 2).
| Staging criteria | Battery type | Charging steps | Charging settings for each stage | Algorithm type | Optimization goals | Advantages | Ref. |
|---|---|---|---|---|---|---|---|
| Based on the upper voltage limit | Li-ion | Five-stage | 1.5 C/1.25 C/0.9 C/0.65 C/0.45 C | TM | Optimal charging current value | This device rapidly charges lithium-ion batteries to 75% capacity over 40 minutes, and it also boasts a 60% longer cycle life compared to the conventional CC–CV charging methods | 65 |
| Li-ion | Five-stage | 1.45 C/1.05 C/1 C/0.7 C/0.1 C | TM | Optimum current value | Enhanced charging speed, improved safety, and extended battery lifespan | 66 | |
| Li-ion | Five-stage | 1.5 C/1.05 C/0.90 C/0.75 C/0.4 C | Grey correlation analysis and TM | Short charging time, minimum peak battery temperature | Reduces charging time and extends battery life | 67 | |
| Li-ion | Five-stage | 1.55 C/1 C/0.6 C/0.3 C/0.2 C | TM | Large charging capacity, high charging efficiency, short charging time | Charging efficiency improved by 0.6–0.9% compared to CC–CV, and battery temperature rise reduced by 2 °C | 68 | |
| Li-ion | Four-stage | 1.262 C/1.082 C/0.662 C/0.258 C | Taguchi particle swarm algorithm | Short charging time, discharge capacity ratio | Charges battery to 94.7% of nominal capacity in 67 minutes | 70 | |
| Li-ion | Three-stage | — | Genetic algorithm | Low temperature rises | Shorter average charging time and lower temperature rise compared to CC–CV | 83 | |
| Li-ion | Five-stage | — | Whale optimization algorithm | Short charging time, low health loss | 34.64% reduction in total fitness function loss and 11.45% reduction in charging time compared to CC charging | 71 | |
| Li-ion | Five-stage | 2.1 C/1.7 C/1.5 C/1.3 C/1 C | Ant colony algorithm | Optimal charging current | Fills up to 70% of the battery in 30 minutes | 72 | |
| Li-ion | Adaptive | Starting from 1 C, then adaptive at each stage | Particle swarm optimization algorithm | Short charging time | Suppresses battery temperature rise and extends battery cycle life | 74 | |
| Based on the SOC | LiPBs | Four-stage | 1.8 C/1.3 C/0.9 C/0.5 C | TM | Optimal charging current | 15 minutes less than conventional CC–CV charging and half the temperature rise variation | 78 |
| LICs | Three-stage | 1.8 C/1.5 C/0.9 C | — | — | Reduced polarization, improved cycling performance, and shorter charging times compared to conventional CC strategies | 79 | |
| Li-ion | Four-stage | 1.4 C/1.0 C/0.7 C/0.4 C | TM | Optimal charging current | Charges Li-ion batteries with different initial SOC with shorter charge times and less temperature variation | 80 | |
| Li-ion | Five-stage | 2 C/1.5 C/1.5 C/1 C/0.5 C | — | — | Enhances the lifespan and effectiveness of lithium-ion batteries | 81 |
The multi-stage charging strategy is a fast-charging strategy specially designed by some early researchers, with the benefits of a long cycle life, and this charging technique is regarded as a high-quality charging option. It is a type of strategy to replace the typical CC–CV charging method that is preferred because of its superior energy efficiency for both charging and discharging, as well as its short charging time, and a series of other advantages.
2.4 Model-based charging strategy
Although traditional charging strategies have been improved in terms of charging speed and efficiency through continuous improvement, these methods require numerous experiments to get the optimal charging strategy. conventional Furthermore, charging strategies fail to meet the user requirement for rapid battery charging under certain circumstances and the battery life requirement considering economic and environmental protection. In addition, traditional charging strategies are often based on empiricism from a macroscopic point of view and increase the charging multiplier to boost charging efficiency. However, these charging strategies do not address the internal mechanism of the battery, which will inevitably cause damage to its health when shortening the charging time. In this case, the changes in the internal substances of the battery can be precisely managed and the safe upper limit of the charging process determined based on the model used to examine the internal mechanism of the battery during charging. It is necessary to decrease the amount of time it takes to charge to an acceptable level to enhance the rate at which the charging occurs, and simultaneously, optimize the longevity and extend the lifespan of the battery.In recent years, an increasing number of scholars have focused on investigating the charging strategies based on battery models via the thorough examination of the important internal physical quantities of the battery in the charging process and optimization with intelligent algorithms. Alternatively, regulation of the charging process is more delicate, and the charging effect achieved is more efficient and reliable, such as charging strategies through the mechanism model and charging strategies based on the ECM.
2.4.1.1 Electrochemical model. Generally, the commonly used electrochemical models are separated into models that use pseudo-two-dimensions (P2D) and models that use a single particle. Doyle et al.84 first proposed the P2D electrochemical model (EM), which describes in detail both solid- and liquid-state reaction processes inside lithium-ion polymer batteries. Pramanik et al.85 established a robust optimized charging strategy to optimize the charging time. Based on P2D-EM, Ouyang86 proposed a non-destructive fast charging strategy for lithium-ion batteries from the internal mechanism of the battery to avoid the phenomenon of lithium metal precipitation during charging by limiting the anode potential of the battery, and the findings indicated that the battery could be charged up to 96.8% within 52 min. Based on this study, the potential of the anode was determined to be within a safe range by the experimental calibration method, according to which an optimized charging strategy was formulated, and the decay rate of the battery was similar to that after 100 fast charging cycles using this charging strategy and after 100 slow charging cycles.87 Also, based on the P2D model, Yin et al.88 suggested an expedited charging approach for lithium-ion batteries considering side reactions by studying the ion transport, interpolation reactions, simplifying the model order, and calculating and limiting the lithium-ion side-reaction current density. Following 100 cycles of charging and draining, a reduction of almost 40% in charging time was observed.
In the subsequent work, as shown in Fig. 13, using the nonlinear model predictive control (NMPC) closed-loop control approach, the charging approach was improved. The optimal current input in the predictive model species was employed to determine the optimal operating temperature associated with the rate of capacity degradation at a given charging current based on the down-ordered electrochemical lifetime model and determine the most efficient charging current at various SOC by updating the terminal voltages and temperatures according to the time variations, which reduced the rate of lithium precipitation and the rate of side reactions.89 To reduce the charging time and the increase battery life, Song et al.90 developed a solution for rapid negative pulse charging using a multi-stage CC charging with a negative pulse to eliminate the polarization phenomenon. Also, starting from a reduced-order simplified EM, Hu et al.91 established an optimized charging strategy based on predictive control of a nonlinear time-varying model, which may successfully prevent the development of adverse effects during charging and improve the charging rate by 22% in contrast to the conventional method of charging CC–CV. Cabanero et al.92 explored non-isothermal electrochemical 3D models to predict when lithium plating would begin, as well as rapid charging techniques for low temperatures. Chaoyang Wang et al.25 developed an electrochemical-physical-aging model that simultaneously considers lithium metal precipitation and the development of films containing a solid electrolyte interphase (SEI) and suggested a technique for asymmetrical temperature modulation that can charge lithium-ion batteries at a high temperature of 60 °C and eliminate the lithium precipitation layer. Also, and the amount of power charged in 10 min could increase the range of an electric vehicle by 200 miles. Goldar et al.93 used a low-complexity EM and conducted experiments at controlled ambient temperatures and uncontrolled scenarios, and the proposed charging strategy speeds up the charging process in comparison to the CC–CV method. The authors in ref. 94 established the optimal charge control constraints considering variables such as the increase in battery temperature, using a single-particle model and electrolyte concentration to obtain a fast charging current profile.
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| Fig. 13 Optimized charging concept based on an NMPC closed-loop control strategy.89 | ||
2.4.1.2 Coupled electrical-thermal-aging model. The combined electric-thermal-aging model includes a thermal model that records the heat-generating behavior of the battery and its relationship with external heat dissipation; subsequently, the electric model uses this temperature to adjust its parameters. The aging model characterizes the loss of capacity of the battery over time. As shown in Fig. 14, these sub-models are interconnected and can fully reflect the operating conditions of the battery.73 Based on the P2D electrochemical model, Qin et al. combined pulse heating with the optimal charging strategy and constructed a coupled thermal-electrical model to calculate the charging performance, and the proposed charging strategy achieved the equivalent rate of 1.5 C and 3 C of lithium non-dissolution at −10 °C and 0 °C, which was 10–30 times faster than the traditional charging strategy, and based this, the charging strategy has an energy efficiency of more than 77%.95 Using an improved thermal behavior model, Zhang et al.96 created an optimum approach for charging that is dependent on the duration and increment in charging temperature, thus halving the charging duration, while leaving the temperature increase unchanged compared to the 1/3C CC–CV charging strategy. Many researchers and scholars have coupled the thermal model with other models to form an optimization model to establish a multi-objective effective charging method for charging more quickly and getting more use out of a battery. You et al.97 applied a multifactor coupled aging model, as depicted in Fig. 15, and a particle swarm optimization technique with two objectives to determine the best low-temperature charging method, and the optimized three-phase CC–CV effective charging approach could strike a balance between the amount of time required to charge and the state of the battery under low-temperature conditions. Xu et al.98 created a model for the corresponding decline in electrochemical thermal capacity and observed a poor charging current profile using a dynamic programming optimization approach. The best practice method lowered the capacity degradation rate by 4.6% and boosted the SEI potential by 57%.
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| Fig. 14 Coupled thermal-electrical-aging model diagram.73 | ||
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| Fig. 15 Schematic diagram of the dual-objective particle swarm algorithm.97 | ||
Wang et al.99 developed an optimized charging technique with three goals of battery life, power consumption, and temperature increase during charging, where the temperature range extended from −5 °C to 45 °C, limiting the increase in temperature by 1 °C to 2 °C and the loss of energy by 150 J during the same charging time. Liu et al.100 established a charging method that offers an ideal charging current profile based on the thermoelectric coupling model, which considered the functional link among charging time, energy loss, and temperature rise, and this charging strategy applies to multiple types of batteries. Gao et al.101 suggested a multi-objective charging system that prioritizes health using a thermoelectric-aging coupling model, fully considering the safety performance, battery life, and charging duration, and the results showed that this strategy can shorten the charging time. Qin et al.95 proposed a fast charging strategy for lithium-independent charging, which showed that the pace of charging was ten to thirty times quicker than the conventional method between −10 °C and 0 °C. By establishing an electrochemical model, a thermal model, and a power-loss model, Zheng Chen et al.102 investigated a method of pricing that considered the battery temperature rise and charging energy consumption. Compared to the conventional charging strategy, with this approach, we could reduce the energy loss by 1.08%, and the temperature increased by 10.47%. As shown in Fig. 16, Xie et al.103 conducted cyclic aging tests considering different charging multiplicities, temperatures, and numbers of cycles and compared the outcomes to that from simulation, and it was determined that it is possible to predict the electrochemical-thermal performance of a battery using the built, very accurate reduced-order electrochemical-thermal coupling model.
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| Fig. 16 Electrochemical model validation.103 (a) Charging end-voltage versus SOC variation characteristics for 1 C multiplicity at different temperatures. (b) Charging end-voltage versus SOC variation characteristics for 3 C multiplicity at different temperatures. (c) Variation in temperature rise of the battery with time versus temperature rise of the battery for different charging multiplicities at 25 °C. (d) End-voltage versus discharge capacity for different numbers of cycles. | ||
The use of mechanistic models can accurately estimate the external characteristics of the battery and some internal characteristics that are difficult to measure, they have potential for use in charging batteries to control the corresponding physical quantities and prevent the occurrence of the reaction of lithium precipitation at the negative electrode, etc. more accurately However, the mechanistic model is complicated, with a large number of parameters, which is very demanding for the arithmetic ability of the BMS, and there is a greater limitation in applying this model in commercial equipment.
ECMs are often associated with optimization problems. Hu et al.104 established ECMs for LiNMC and LiFePO4, solved the optimization problem by using an adaptive mesh refinement algorithm, and investigated the effects of the voltage thresholds, temperatures, and aging on the optimization results. Guo et al.105 used temperature, SOC, and voltage as constraints to establish an ECM and optimized it using a genetic algorithm to maximize the charging efficiency to obtain a nonlinear varying current profile. By utilizing an analogous circuit model and capacity decay analysis, Liu et al.106 developed a perfect charging method that mixed fourth-stage CC charging with brief CV charging, and the results showed that adding a shorter CV stage to the MSCC charging can increase the charging capacity and save time; additionally, it can extend the lifespan of the battery. Xavier et al.107 used a modified form of the model predictive control (MPC) algorithm to mimic the ohmic internal resistance of a battery for prediction using a first-order RC-equivalent circuit model in discrete-time state space based on real parameters. In a subsequent study, the MPC algorithm was further improved by setting the control range and prediction range control input to decay exponentially, relaxing the simplified assumptions of the standard MPC for exceeding hard constraints outside the range. As shown in Fig. 17, the charging time was even shorter compared with the standard MPC algorithm by adding a step to the control increment vector outside the control field of view.108 Chen et al.109 developed with a charging strategy to reduce the battery charging losses based on the first-order resistance-capacitance circuit ECM. The optimized charging strategy effectively reduced the losses during charging (Table 3).
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| Fig. 17 Optimization flow for the standard MPC algorithm.107 | ||
| Charging division of different models | Battery type | Optimization goals | Application of algorithm | Specific adoption of models | Comparing charging strategies | Advantages | Ref. |
|---|---|---|---|---|---|---|---|
| Based on mechanistic models | Li-ion | State of charge, solid phase concentration, molar flux, cell temperature, and over-potential | Pontryagin's minimum principle | EM | CC | Significantly reduces charging time and extends battery life | 85 |
| Li-ion | Charging current, over potential | Non-destructive fast charging algorithm | SP2D model | — | Protect the health of battery, while reducing charging time | 86 | |
| Li-ion | Different surface ion concentrations, states of charge, cut-off voltages, and side reaction rates | Extended Kalman filter | Reduced-order electrochemical modeling | CC–CV | Reduced degradation rates and shorter charging times | 88 | |
| Li-ion | Battery operating temperature | — | Reduced-order electrochemical modeling | CC–CV | Shorter charging time and longer cycle life | 89 | |
| Li-ion | Battery life and charging time | A fast-moving horizon estimation algorithm | EM | CC–CV/MCC | Improved charging efficiency | 91 | |
| Li-ion | Charging time and battery temperature rise | Genetic algorithm | Thermal modeling | CC–CV | Achieves a good balance of charging speed and longevity | 96 | |
| Li-ion | Charging time, energy loss and battery temperature rise | NMPC | Electro-thermal coupling model | Multi-stage CC | Reduce 150 J energy loss and 1–2 °C temperature rise in the same charging time | 99 | |
| Li-ion | Battery life and charging time | — | Coupled electrical-thermal-aging model | CC–CV | Reduced charging time without sacrificing battery health status | 101 | |
| Li-ion | Charging temperature rise, energy loss, and charging time | Genetic algorithm | EM, battery power loss modeling, and battery thermal modeling | CC | The relationship between optimal charging current, internal resistance, and polarization resistance is revealed; the influence of temperature on battery parameters is considered to ensure a wide application temperature window | 102 | |
| Li-ion | Charging time | Extended Kalman filter | Equivalent hydraulic model | CC–CV | Reduced charging time | 93 | |
| Li-ion | Battery health and charge time | Particle swarm optimization algorithm | Coupled with modeling of aging | CC–CV | Improvement of charging performance of lithium-ion batteries in low-temperature environments | 97 | |
| Li-ion | Charging speed and charging efficiency | Grey forecast | Grey model | CC–CV | Charging speed increased by more than 23% and battery charging efficiency increased by more than 1.6% | 98 | |
| Based on an ECM | LiFePO4 | Charging time and charging losses | Adaptive mesh refinement algorithm | ECM | CC–CV | Minimizes charging time and energy loss | 104 |
| Li-ion | Charging time | Genetic algorithm | ECM | CC–CV | Improves charging efficiency and battery cycle life | 105 | |
| Li-ion | Charging time | MPC | ECM | — | The shortest charging time can be solved under multiple constraints | 107 | |
| Li-ion | Charging time | MPC | ECM | — | Reduced battery charging time from initial SOC to final value | 108 | |
| Li-ion | Charging losses | DP | ECM | CC | Effective reduction of charging losses | 109 |
According to the review of four different strategies of CC–CV, pulse charging, multi-stage charging and model-based charging, the CC–CV charging approach incorporates model-based charging and offers a straightforward charging procedure, but polarization can occur when charging, which may potentially harm the battery. While charging, the pulse charging method can remove the polarization voltage of the battery and its temperature increases, leading to the production of more heat. This elevated temperature can negatively impact the overall health and well-being of the battery. The multi-stage charging strategy divides the current value of different stages, generally using the stepwise decrease in current for charging, where the charging time is shorter and the charging capacity is larger. Model-based charging makes use of the mechanism model and ECM and optimizes the algorithm to pay more attention to the modifications made to the internal mechanism of the battery to optimize the charging time without compromising the health and cost of the battery.
3. Power battery module charging strategy
In practice, because the specified voltage and capacity of an individual lithium-ion battery are limited to meet the needs of high-power-use scenarios, connecting numerous batteries is essential according to the actual power requirements to form a larger battery module to satisfy the power and energy demands of an electric car. The four types of battery modules commonly connected to configure this are shown in Fig. 18. Hence, it is imperative to investigate the battery charging technique, with particular emphasis on its influence on the battery system module and its capacity characteristics. | ||
| Fig. 18 Cell connection configurations in a battery pack: (a) series connection; (b) parallel connection; (c) series followed by parallel connection; and (d) parallel followed by series connection. | ||
Presently, the impact of factors such as charging multiplicity and temperature on the properties of battery modules during charging has emerged as a prominent subject of research. Li et al.110 proposed the variation characteristics of the charging temperature of battery modules under two cooling methods, liquid immersion cooling and forced air cooling, for both 2 C and 3 C charging multiplicity. The results show that when using 2 C and 3 C charging multiples, liquid immersion cooling lowers the peak temperature of the module by 7.7 °C and 19.6 °C, in contrast to cooling by forced air, respectively. Liu et al.111 proposed a method for charging the battery system module that utilizes a liquid-heated thermal management system. This method involved preheating the battery before formal charging to reduce the overall charging time and the thermal model and offline characteristics of the battery pack system were integrated to create a simulation optimization model. Using this charging strategy, a battery system module with an initial temperature of −10 °C could be formally charged by preheating to 1 °C, which significantly reduced the charging time. Kalogiannis et al.112 used incremental capacity analysis to track the characteristics of charging current versus temperature within a battery system module, using a battery system module with different capacities of single cells in series based on different test temperatures and different charging multiplicities, all proving the effectiveness of the incremental capacity analysis.
To achieve equilibrium between the temperature disparity and the duration required for charging, it is necessary to address the battery system module. Fan et al.113 created a thermal model and suggested applying an enhanced charging approach to the battery system module based on a genetic algorithm. The use of the MS CC–CV charging approach resulted in a significant reduction of 37.9% in the temperature difference of the battery module and the charging time was reduced by 11.9% in comparison to CC–CV charging. The authors in ref. 114 created a monitoring system to prevent lithium plating in parallel-connected battery modules when charging them, which decreased the likelihood of lithium plating when the battery is being charged and shortened the total charging time by 18%, in addition to reducing the peak battery temperature by about 9.8% in comparison to the traditional CC–CV charging technique. Sun et al.115 investigated the temperature and inhomogeneous heat problem of the battery system module under rapid charging circumstances and a computational fluid dynamics (CFD) study was conducted to examine how the temperature of the battery system module was affected by the following variables: channel width, coolant flow rate, and coolant temperature, and it was verified by simulation and experiment that the temperature of the battery was positively affected by both the flow rate and temperature, leading to a maximum temperature reduction of 10.93% and 15.12%, respectively. Chen et al.116 proposed that the battery system module can be safely and quickly charged in fast-charging environments, where the spacing between the battery cells, the transverse and longitudinal width of the channel, and the NSGA II genetic algorithm were used to do a multi-objective optimization, while considering the channel depth and other parameters, which yielded that the depth of the channel had the greatest influence. Qin et al.117 established a high-precision simulation model for ultrafast charging and suggested a system that uses circulating cooling equipment and cooling plates to externally cool liquid lithium-ion battery modules, which can effectively control the temperature rise.
The impact of a charging strategy on the charging capacity of a battery module based on the its evolution law concerning temperature rise and charging time is explored by considering parameters of temperature and charging time. Yu et al.118 proposed an electrolyte, degradation, and temperature model for a battery that was monolithic with a single-particle component. Employing the minimal lithium plating overpotential control charging philosophy, the charging current of the battery system module prevents the formation of lithium plating sites, and this charging strategy can lessen the effect of lithium plating on capacity loss, but the charging time is longer. Li et al.119 combined the battery monomer model-based cooling model for the battery system module and the balance management model with electro-thermal-aging coupling to form a complete battery system module model. Fig. 19 shows the model optimization process for the adaptive CC–CV charging strategy, which considers the effects of charging time, battery age, and energy loss. In the first step, the parameters of the algorithm are set first to determine the weight coefficients and the battery constraints; in the second step, after inputting the current, the values of the parameters of the voltage, SOC, SOH, temperature, etc. can be obtained through the model calculation. Step three involves finding the minimum value of the objective function, finally outputting the optimized result value. This strategy significantly reduces the charging time but makes the battery aging severe and increases the energy consumption. Ojo et al.120 advocated for a pricing plan that integrates active equalization and health awareness, and this strategy enables rapid equalization of all modules in a short time while making full use of the charging potential of the cell. Also, this strategy speeds up the charging process by 16.3% in comparison to the CC–CV method, and compared to passive equalization, the use of active equalization increases the available capacity of the battery system module from 10% to 15.3%. Hannan et al.121 proposed a battery system module equalization charging algorithm to prolong the life and enhance the safety of the battery, and the experimental findings demonstrated that the algorithm for charge equalization controllers worked effectively in undercharging or overcharging situations. Guo Lei et al.122 found that the temperature drops throughout the study, and the charging performance of the battery also declines, and thus proposed the method of heating the battery using a wide wire metal film to improve the efficiency of the battery under low-temperature conditions.
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| Fig. 19 Adaptive constant current and constant voltage charging strategy optimization flow.119 | ||
Ref. 123 proposed a fast charging control method by combining a charger and a balancer to control the battery system module, and designed a two-layer optimization strategy using multi-objective optimization considering charging time, energy consumption, charging constraints, etc. to obtain the optimal charging current and balancing current, and the outcomes of the simulations and experiments demonstrated the superiority of this charging strategy. In addition, the authors designed a charging strategy based on two-tier hierarchical control considering aspects such as battery equalization, charging temperature, and other user requirements in the top-level design.124 In subsequent research work, they again proposed a distributed charging strategy based on the leader-follower approach, which can online compensate for the divergence of the battery from the user demand and the energy loss in the system module to accomplish charging control of a series-connected lithium-ion battery system module, which can reduce the number of calculations of charger controllers and improve the robustness.125 Song et al.126 designed a charge equalization strategy to enable the use of the charging voltage profile to fix the inconsistent performance of the battery system module, which can maximize its capacity and enhance the consistency of its capacity significantly between the new and aged batteries. The equalization strategy has high accuracy and low computational effort and can be used in online equalization for EVs.
As a means of investigating power battery pack charging strategies, more researchers and scholars focus on the time it takes to charge, the charging multiplier, the incremental increase in the module temperature, the temperature difference inside the module, and other parameters with the charging change characteristics, where the use of a reasonable charging strategy can achieve thermal balance within the module, which safeguards the battery.
4. Electric vehicle charging strategy
An integral part of developing EVs is exploring various electric car charging mechanisms and their potential uses. One of the most common ways to charge a fully electric vehicle is with the entire vehicle charging mode (including AC slow charging and DC fast charging), wireless charging mode, and rapid power exchange mode. Different charging modes have different charging characteristics and application scenarios, and customers often choose charging modes according to their individual needs, the study of which can better guide the charging market to develop in an orderly manner.4.1 AC slow charging
The onboard charger in an electric vehicle charges the battery by converting incoming AC power to DC electricity. AC slow charging has many advantages such as a small charging current, small size of charging facilities, simple structure, flexible deployment, low cost of charging pile construction, easy maintenance, high safety, simple operation, and easy installation, but its charging time is long. Straka et al.127 used a data-centric approach to analyze the environment surrounding AC slow-charging infrastructure and concluded that its functions and characteristics affect its power consumption distribution. Kim et al.128 researched electric vehicle charging using the AC slow-charging mode according to the charging model, as shown in Fig. 20, employing Class 3 kW conditions when charging by the traditional 220 V socket to receive AC power through the connector to the electric vehicle charger. According to the comparison of Class 3 kW and Class 7–11 kW slow charging conditions, it was found that the Class 3 kW performance is better and its cost is lower. Bao et al.,129 in response to the shortcomings of AC charging stacks where the harmonic current of the on-board charger cannot be reduced, proposed the incorporation of the filtering function into the design of slow-charging AC charging piles to filter the harmonics in the current and inhibit the effects of harmonics on the electricity bill. Liu et al.130 developed a two-tier management mechanism based on heating, ventilation, and air conditioning, where the first tier considers the economization of electricity, heat, and gas, and the second tier manages the AC slow charging of EVs using real-time supply and demand requests. | ||
| Fig. 20 Conversion process for charging a battery using alternating current.128 | ||
4.2 DC fast charging
The DC fast charging mode, through external charging facilities, inserts DC power into the power battery after converting AC power from the national grid, and in actual use, three-phase AC power is generally used as the power supply. DC rapid charging has many desirable qualities, such as high charging efficiency, high power output, and high voltage. Nevertheless, the battery experiences a significant increase in temperature when being charged and the rate capacity is reduced, which damages the health of the battery, and thus this mode is suitable for urgent traveling needs.131 The use of a DC fast charging mode charging pile can make the charging station more efficient and lessen the likelihood of having to wait in a queue. Usually, the charging pile in question is well-suited for use in certain regions with high demand density. Prolonged high-current fast charging can also impact public grid equipment, affecting the quality and safety of the power supply.Wu et al.132 designed a DC charging pile with multiple charging modules connected in parallel to enhance the charging speed and charging power, and the simulation and experimental findings demonstrated the efficacy of this DC charging station. Braunl et al.133 identified the location, size, and quantity of public DC fast charging stations required to fulfill the electrical demands of their electric vehicle fleet based on actual traffic data and toll demand estimation methods in Western Australia. Fu et al.134 considered the states of charging and discharging of EV charging stations and developed a linear model of a DC distribution system. Connecting multiple EV charging stations in the charging state, the instability of the DC distribution system increased and the stability of the system was the lowest when all the charging stations were operating at the maximum charging capacity. Mokgonyana et al.135 proposed the optimization of the deployment of a DC charger network by constructing a versatile energy management system to minimize electricity expenses, where it is important to consider the whole quantity of chargers, network dispensability, and energy management characteristics. Cleenwerck et al.136 compared the energy loss and voltage stability of low-voltage (LV) DC microgrids with AC microgrids through power flow analysis and proposed a method for charging LV distribution networks with LVDC backbones.
The use of photovoltaics in charging systems will significantly reduce the cost of electricity, as shown in Fig. 21. Muratori et al.137 quantified the configuration of the least costly technological solution for the full lifecycle of a DC fast charging station, deploying solar photovoltaic panels in tandem with batteries as a technological solution for reducing the cost of DC fast charging electricity for EVs. Utilizing the photovoltaic system reduces the cost and the photovoltaic system can efficiently handle new loads related to solar power, even in areas where solar power is a minor resource. Wang et al.138 simulated various driving scenarios and designed a DC microgrid charging station management strategy based on power limitation, which includes photovoltaic energy to prevent overloading of the grid during usage. To widen the voltage range of DC charging, Shi et al.139 designed a charger based on the integration of dual inverters, which can be utilized for the rapid charging of electric vehicles over a DC distribution network, with a wider range of voltages, and can effectively control the current and maintain the energy balance. Kim et al.140 proposed a high-performance voltage equalizer incorporating a DC–DC converter for rapid charging. Compared to the conventional voltage balancer, this balancer has no limitation in voltage balancing control range and has lower switching voltage and higher efficiency.
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| Fig. 21 Least costly technology configuration for fast charging across sites based on load variation.137 | ||
4.3 Wireless charging
Wireless charging mode refers to the use of space as the energy transmission medium for charging without a wire connection. Compared with the widely used direct contact technology of charging, the wireless charging mode can not only reduce the cost of power cables and other connecting devices, but also avoid repeated unplugging and plugging of wires during the charging process to produce consumables or electric shock, etc. Also, the wireless charging mechanism in energy transfer has a significant level of versatility, high reliability, high-security features, and remains unaffected by the surrounding conditions.The study of coils in wireless charging systems has become a hot topic nowadays. Mohamed et al.141 constructed a high-precision mathematical model by considering the number of turns, the shape of the coil, the topology compensation used, and the state of the system, which can accurately obtain the charging power required for wireless charging. Razu et al.142 investigated the effect of distance between neighboring coils on wireless power transmission, where vertical and horizontal misalignments were simulated by building a system consisting of two Archimedean copper coils, yielding a transmission power of 3.74 kW for a 150 mm airgap, with a transmission efficiency of 92.4%. As shown in Fig. 22, Machura et al.143 developed a wireless power transfer system for electric vehicles, outlining its primary components and compared the key technologies in wireless charging technology such as compensation topology, coil design, and communication techniques. Matjaz et al.144 suggested an intelligent electric vehicle charging system that minimizes energy waste during coil optimization by automatically determining the position of the receiver, and thus the charging mat uses the coil with the highest transmission efficiency to charge it.
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| Fig. 22 Main system components for wireless power transmission in electric vehicles.143 | ||
Battery size and battery state are inextricably linked to the efficiency, safety, and economy of the wireless charging process. Machura et al.145 used a vehicle model in tandem with a battery model to mimic how wireless charging impacts the condition of the battery under three driving scenarios including motorway, city road, and their combination. The authors in ref. 146 evaluated the cost of dynamic wireless charging EVs using a given size of battery based on a mathematical model, incorporating a battery degradation model to measure how the spread of charging stations affects the length of the battery life and analyzing the relationship between economic cost and battery size. The wireless charging method proposed by Huang et al.,147 in addition to conventional power transmission, can also realize the transmission of information such as emergency messages, vehicle identity document (ID) codes, and battery status between the power grid and the vehicle, which can monitor the vehicle status in real-time, and adjust the charging current size to guarantee a safe and efficient wireless charging process in real-time based on the battery condition.
To prevent damage to the device during wireless charging, Zhou et al.148 designed a detection circuit by measuring the current output from the inverter and its phase angle change and used tests to prove the effectiveness and reliability of this detection circuit. Majhi et al.149 considered the relevant parameters affecting driver charging decisions and developed a model for optimizing the deployment based on mixed integer cost and cost investment in dynamic wireless charging facilities, and individual charging costs for optimal economic efficiency. Hwang et al.150 constructed a general model to determine the best way to distribute power tracks and calculate the battery capacity for each route.
4.4 Fast power charging
The rapid power exchange mode for EVs is to swap out the fully charged battery of a vehicle, where the battery with power loss is charged in a power exchange station. This power exchange mode shortens the time of electric energy replenishment and is the mode with the highest charging efficiency at this stage.151 The replaced battery can adopt a slow charging strategy in the exchange station, and thus it does not suffer any harm from rapid charging. Also, the battery status can be detected in the exchange station, and the battery can be overhauled according to the actual situation, enabling it to last as long as possible.Some research focuses on the micro perspective, considering the behavioral habits of EV owners, different modes of transport, and other factors that affect the characteristics of system reliability and service capacity. Cheng et al.152 introduced the user demand unmet reliability metrics and the principle of power switching is shown in Fig. 23. Zhang et al.153 developed stochastic models for taxi, bus, charging station, and battery exchange systems. The service capacity of EV power supply equipment was compared, and the effects of battery capacity, traveling speed, charging station power, and exchange service price on the service capacity were investigated.
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| Fig. 23 Schematic diagram of power switching mode.153 | ||
The construction cost and operation status of the power exchange station are directly related to the promotion and application of the power exchange mode, and thus improving the efficiency and utilization rate of the power exchange station and increasing its operation income have become the key points of research. Wang et al.154 established a hybrid battery exchange system based on the combination of two modes, mobile battery exchange and stationary battery exchange. The sigmoid function was utilized to compute the level of uncertainty in EV selection, the scheduling of EVs with switching demand, and improving their efficiency. Shalaby et al.155 proposed a rolling level optimization-based approach to provide optimal battery exchange and discharge processes to achieve dynamic scheduling of the battery exchange station, and thus maximize the daily profit. The proposed dynamic scheduling was simulated and verified to improve the profit by 10% to 25.7% over the daily scheduling. Adler et al.156 established a method of reserving batteries to improve the efficiency of battery replacement, where vehicles can minimize the average delay across all vehicles by making detours to avoid charging stations with no available batteries or by reserving them for vehicles needing to replace their batteries. Zeng et al.157 developed a battery switching station operation model that includes the calculation of available generation capacity and established a rolling optimal dispatch model for the distribution network, based on which an evaluation framework was proposed to analyze the battery switching station in terms of both economy and reliability. You et al.158 proposed an online allocation algorithm for battery exchange stations based on the location of EVs, road traffic conditions, and battery availability at the battery exchange stations, intending to reduce the cost of EV services and decongest the battery exchange stations. Li et al.159 developed a centralized battery scheduling technique for an exchange scenario, constructed a battery procurement cost optimization model considering the peak charging load of the battery, and simulated the centralized battery scheduling for battery replacement under a specified scenario using 1000 sample vehicles. The results showed that the battery procurement cost using the centralized scheduling strategy is 4.15 million RMB and the peak charging load is 2418 kW. Yang et al.160 suggested a collaborative battery station model, where customers can rent batteries and pay according to the amount of power demanded and the satisfaction of the service to enhance the responsiveness of the shared battery station and better adapt to the customer's needs, where an adaptive scheduling strategy was designed to maximize revenue, and it is necessary to optimize the charging, discharging, and inactive activities of the batteries.
In the case of the AC slow charging mode with low power and slow charging speed, the charging time is longer, which is suitable for charging vehicles during a long period of stop and go. Alternatively, the DC fast charging mode is characterized by a high charging voltage, power, and charging efficiency, but its charging cost is also higher and it easily damages the battery, making it more suitable for emergency travel demand and other scenarios. Wireless charging does not need to be connected through cables, which is the most convenient charging mode, but there will be problems such as radiation leakage, and safety needs to be improved. The fast power exchange mode shortens the power supply time and has high efficiency, but current power exchange technology is still immature, with high implementation costs, low coverage, and poor versatility. Only the combination of fast charging piles, slow charging piles, wireless charging, and power exchange stations can better facilitate the expedited advancement of alternative energy vehicles.
5. Charging strategy intelligent development trends and prospects
Lithium-ion power batteries serve as the primary power source for electric vehicles, and thus the advancement of electric car charging technology is crucial for the advancement of lithium batteries as a source of energy storage. Also, in many applications, the charging strategy is related to the stability and safety of the grid. According to the previous summary and research on lithium-ion single-cell batteries, modules, and electric vehicle charging strategies, the research progress is shown in Table 4.| Type of study | Charging strategy | State of the art |
|---|---|---|
| Lithium-ion power single-cell battery | CC–CV | Li-ion batteries exhibit high and low-temperature operating characteristics with temperature changes. The lower the internal resistance, the less power the battery needs to produce current.24 |
| CC–CV | When the current exceeds 0.4 C, although it will not dramatically reduce the charging time, boosting the current will make the lithium plating worse.40 | |
| Pulse charging | The pulse amplitude can be optimized by minimizing ohmic losses following the battery's intrinsic resistance characteristics.51 | |
| Pulse charging | Duty variable voltage pulse charger (DVVPC) enhances the battery charging performance by detecting and dynamically tracking the ideal charging pulse duty cycle, this charging technique increases speed by 14%, while increasing the charging efficiency by 1.5% compared to the conventional pulse charging method with a 50% duty cycle.59 | |
| Multi-stage charging | After just 75 minutes of charging, the battery reached 40% capacity due to this method, and this charging strategy measured a 60% improvement in battery cycle life.65 | |
| Multi-stage charging | In a four-stage (25% SOC as an interval) charging strategy, the current magnitude of each stage is determined by an orthogonal array technique. Charging reaches SOC = 100% when charging ends and suggests that the battery is now completely charged.78 | |
| Model-based charging | Based on the P2D model, a fast-charging strategy for lithium-ion batteries considers side reactions by studying ion transport, and interpolation reactions, simplifying the model order, and calculating and limiting the lithium-ion side-reaction current density.88 | |
| Model-based charging | Thermoelectric coupling theory also took into account the functional connection between charging duration, energy loss, and temperature increase.100 | |
| Power battery module | — | Li et al.110 investigated the variation characteristics of the charging temperature of battery modules under two cooling methods, forced air cooling and liquid immersion cooling, for both 2 C and 3 C charging multiplicity |
| — | Fan et al.113 proposed an optimized charging strategy applied to the battery system module based on a genetic algorithm. A decrease of 37.9% in the temperature differential between the battery module and the charger and an acceleration of 11.9% in the charging duration | |
| — | Qin et al.117 established a high-precision simulation model for ultrafast charging and proposed a technique that uses circulating cooling equipment and cooling plates to externally cool lithium-ion battery modules, successfully controlling the temperature rise | |
| Electric vehicle | AC slow charging | Kim et al.128 investigated using the charging model, the procedure for charging electric vehicles using AC slow-charging mode. To compare, sluggish charging circumstances for Class 3 kW and Class 7–11 kW and found that the performance of Class 3 kW is better and its cost and lower |
| AC slow charging | Bao et al.129 proposed the incorporation of the filtering function into the design of the slow-charging AC charging piles to filter the harmonics in the current and to inhibit the impact of the harmonics on the billing of the power system | |
| DC fast charging | Wu et al.132 designed a DC charging pile with multiple charging modules connected in parallel to enhance the charging speed and charging power | |
| DC fast charging | Shi et al.139 designed a charger based on the integration of dual inverters, which is suitable for charging electric automobiles quickly, using a DC distribution network, with a wider range of voltages, and can effectively control the current and maintain the energy balance | |
| Wireless charging | Machura et al.143 summarized the main components of an electric vehicle wireless power transfer system and compared the key technologies in wireless charging technology such as compensation topology, coil design, and communication techniques | |
| Wireless charging | Hwang et al.150 developed a general model to achieve optimal power track allocation, calculate car battery sizes for each route, and resolve the optimization issue of a multi-path, dynamic wireless EV charging system using a particle swarm optimization technique | |
| Fast power charging | Wang et al.154 established a hybrid battery exchange system based on the combination of two modes: mobile battery exchange and stationary battery exchange | |
| Fast power charging | Zeng et al.157 developed a battery switching station operation model that includes the calculation of available generation capacity and established a rolling optimal dispatch model for the distribution network |
Presently, the development in the battery industry is focused designing more scientific and reasonable charging strategies according to the charging demand, and the process of designing an intelligent charging strategy is showing in Fig. 24. The first step: determining the optimization goal. In terms of the single battery charging strategy, the current charging optimization has a single target and other problems, where the optimization target can be designed as low charging temperature increase, large charging capacity, short charging time, long battery life small energy loss, and other multi-objectives. The charging objectives of the battery module mainly include strong charging consistency, fast equalization control, low charging temperature increase, large charging capacity, short charging time, long battery life and low energy loss. Step 2: intelligent charging optimization scheme. This step mainly consists of four parts, as follows: (a) selecting the applicable charging method, which can be charged by CC, CV, CC–CV, current pulse, voltage pulse, variable current, constant power constant voltage, and other charging methods. (b) Setting the constraint boundaries in the charging process, which usually can be set up with the conditions of upper cut-off voltage, different SOC intervals, different SOH intervals, lower cut-off current, etc.; (c) adopting scientific algorithms. To meet the demand for multi-objective charging, it needs to be selected according to the type and structure of the collected data, as well as the algorithm calculation volume and precision. (d) Selection of the model, where the commonly used models are the equivalent circuit model, electrochemical model, and electric-thermal-aging coupling model. The optimal charging strategy that meets the target demand is designed to show the superiority of the charging strategy in different charging application scenarios, such as battery single, battery module, and vehicle charging.
 | ||
| Fig. 24 Design flow of optimized charging strategy for lithium-ion power batteries. | ||
Fast charging and intelligent charging will be the main development trend in the future.
(1) Multi-stage adaptive charging strategy. According to the internal and external charging temperature, battery health state, and charge state, accurately grasping the lithium precipitation extreme point of the battery during the charging process to adaptively regulate the charging current and voltage, realize the shortening of the charging time and prolong the service life of the battery without damaging its health.
(2) Rapid active intelligent control strategy. Streamlined and accurate control strategies and simplified circuits are used in the battery module charging process, and machine learning is used to construct a model that characterizes the inconsistency of the battery pack, and thus the charging conditions of the battery module can be better managed, and abnormal single or module batteries can be quickly identified to prevent thermal runaway phenomena in batteries.
(3) Based on charging data, the power battery health state and working condition can be rapidly detected. Given electric vehicle power battery detection needs, the development of electric vehicle power battery health state rapid detection by integrating multi-dimensional charging data features, constructing a comprehensive working condition for battery health state detection based on electric vehicle operation scenarios, and injecting a sequence of detection conditions into the traditional power battery charging working condition can be achieved. The machine learning method is used to establish a rapid battery health state detection model based on multi-dimensional feature parameter input, without the need for complete charging and discharging of the battery, there is no restriction on the initial battery charge, and in the single charging process, the use of condition data combined with the detection algorithms can be completed to quickly detect the health state of the power battery.
(4) Battery charging safety protection based on big data driving. To solve the characteristics of battery charging faults with hidden features, random occurrence time, indefinite duration and changeable scenes, the multi-dimensional feature parameter extraction method for battery charging safety based on expert system and big data mining technology, a database of battery safety feature parameters and multi-scale charging safety assessment models can be constructed based on statistical analysis, machine learning, and big data-driven technology to achieve the inconsistency in voltage in the charging process of the battery. Real-time diagnosis and prevention of temperature anomalies and short-circuit failures during the battery charging process will help accurately assess the health status and residual value of battery packs and systems, and facilitate users and manufacturers to carry out maintenance, replacement, or upgrading.
(5) Application expansion. From the perspective of practical application needs, research on the smart charging strategy is expected to be applied to equipment charging, grid system energy storage, and battery functional composite design.
6. Conclusions
Herein, we systematically reviewed and compared the current lithium-ion power battery single and module charging strategies, laying the foundation for further development and optimization of new charging strategies. In addition, a comprehensive comparison of electric vehicle charging application scenarios and different charging modes was presented, which provides some guidance for electric vehicles to choose charging modes in different scenarios. Finally, the development trend of intelligent charging strategies was discussed and researched, and the design process of optimized charging strategies was proposed, providing a reference for finding the optimal charging strategies.(a) Starting from the four strategies of CC–CV, pulse charging, multi-stage charging, and model-based charging, the characteristics of various charging strategies and the current research status were summarized. The four types of charging strategies summarized have different effects on enhancing the charging performance, and more applicable charging strategies can be selected according to specific charging objectives.
(b) The power battery module is an important part of the power battery system, and herein we discussed and studied the battery characteristics during the charging process under different charging strategies in terms of charging multiplier, charging temperature, charging time, and charging capacity.
(c) In the actual charging application of EVs, AC slow charging, DC fast charging, wireless charging, and rapid power exchange have become the four main charging modes guiding the development of the charging market. These four charging modes apply to different travel or charging needs, and improving the charging efficiency, shortening the charging time, and safeguarding the health and safety of batteries are the common goals of each charging mode.
(d) In the case of battery cells and battery modules, formulating charging targets, it is necessary to consider multiple influencing factors and design optimized charging strategies based on algorithms and models. The multi-stage adaptive charging strategy and rapid active intelligent equalization control strategy are the future directions of the lithium-ion power battery charging strategy.
In recent years, the development of EVs has been rapid to break through the range of bottlenecks, where the lithium-ion power battery as the core component of the vehicle has been a hot spot for research and development. Due to the wide range of charging application scenarios, it has become an urgent need to formulate a charging strategy that is more flexible and adaptable within the safety range and meets diversified charging needs as much as possible, and thus it is necessary to design an adaptive charging strategy, a fast and active intelligent control strategy, and an intelligent control strategy that can be used to control the charging process.
Author contributions
Shan Li: conceptualization, methodology, software, formal analysis, writing – original draft, writing – review & editing. Jian Ma: conceptualization, funding acquisition, resources, supervision, writing – review & editing. Xuan Zhao: conceptualization, funding acquisition, resources, supervision, writing – review & editing. Kai Zhang: data curation, software, writing – review & editing. Zhipeng Jiao: software, visualization, investigation. Qifan Xue: visualization, investigation.Conflicts of interest
There are no conflicts to declare.Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (52172362), Major Science and Technology Projects of Shaanxi Province (2020ZDZX06-01-01), Transformation of Scientific and Technological Achievements Program of Shaanxi Province(2024CG-CGZH-19).Notes and references
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