Mohammed Asiria,
Munthar Kedhimbij,
Vicky Jainc,
Suhas Ballald,
Abhayveer Singhe,
V. Kavithaf,
Nargiza Kamolovag and
Milad Nourizadeh*h
aDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
bCollege of Pharmacy, The Islamic University, Najaf, Iraq. E-mail: muntherabosoda@iunajaf.edu.iq
cMarwadi University Research Center, Department of Chemistry, Faculty of Science, Marwadi University, Rajkot-360003, Gujarat, India
dDepartment of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India
eCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, India
fDepartment of Chemistry, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
gDepartment of Chemistry and Its Teaching Methods, Tashkent State Pedagogical University, Tashkent, Uzbekistan
hYoung Researchers and Elite Club, Ilam University, Ilam, Iran. E-mail: miladnourizadehacademic@gmail.com
iDepartment of Medical Analysis, Medical Laboratory Technique College, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
jDepartment of Medical Analysis, Medical Laboratory Technique College, The Islamic University of Babylon, Babylon, Iraq
First published on 2nd July 2025
This study utilizes a Pseudo-Two-Dimensional (P2D) model to predict calendar aging in LiFePO4/graphite lithium-ion batteries, emphasizing temperature and state-of-charge (SOC) impacts. Implemented in COMSOL Multiphysics, the P2D framework simulates solid electrolyte interphase (SEI) growth and electrolyte conductivity loss, driven by parasitic redox reactions at the electrode–electrolyte interface, modeled using Arrhenius and Tafel kinetics. Validated against experimental data across five temperature–SOC conditions, the P2D model achieves root mean square errors below 0.9. Results show synergistic degradation, with SEI thickness exceeding 300 nm and conductivity loss over 20% after 36 months at 55 °C and 90% SOC. Higher SOCs intensify SEI growth due to electrolyte instability at elevated anode potentials. This P2D-based, chemically grounded approach provides mechanistic insights into storage degradation, enabling optimized battery management and storage strategies to enhance lifespan and reliability for electric vehicles and grid applications.
Aging mechanisms in lithium-ion batteries (LIBs) can be broadly classified into two fundamental categories: cycle aging and calendar aging. Cycle aging arises from repetitive electrochemical cycling (i.e., charge and discharge processes) which impose mechanical, thermal, and chemical stresses on the electrodes, leading to degradation phenomena such as active material loss, structural collapse, particle cracking, and lithium inventory depletion due to repeated intercalation/deintercalation cycles.8–10 This aging mode is primarily driven by electrochemical, mechanical, and thermal stresses exerted on the electrode materials during the intercalation and deintercalation of lithium ions. Over time, these stresses lead to phenomena such as active material loss, microstructural damage, SEI instability, and lithium inventory depletion, ultimately resulting in capacity fade and increased internal resistance.9–11 Recent studies have highlighted that the rate and nature of cycle-induced degradation are strongly dependent on operational parameters such as depth of discharge (DoD), current rate (C-rate), temperature, and state-of-charge (SOC) window.8,12 Therefore, accurate modeling and characterization of cycle aging are essential for predicting battery lifetime and guiding the development of optimized cycling protocols that mitigate long-term degradation.13,14
In contrast, calendar aging refers to the gradual degradation of battery performance over time when the cell is stored at a constant state-of-charge (SOC), with no external current flow. This aging mode is driven primarily by parasitic side reactions occurring at the electrode/electrolyte interfaces, particularly at the anode side, even under open-circuit conditions. These reactions are accelerated by elevated temperatures and high SOC levels and contribute to the irreversible loss of lithium, resulting in capacity fade and increased internal resistance over time.6,15,16
The dominant degradation mechanisms involved in calendar aging include:
• Growth of the solid electrolyte interphase (SEI) on the anode surface due to continuous electrolyte reduction reactions. This process consumes cyclable lithium and increases ionic/electronic resistance.
• Electrolyte decomposition, which leads to the formation of gas and insoluble byproducts, reducing ionic conductivity.
• Passive layer formation and structural degradation of active materials due to slow but persistent reactions with electrolyte solvents or dissolved transition metals.9,11,12,17,18
Among these, SEI layer growth is recognized as the most significant contributor to calendar aging in graphite-based anodes.19 While the SEI initially forms during the first few cycles to stabilize the anode/electrolyte interface, its continued growth during storage consumes lithium ions and thickens the interfacial resistance layer.20,21 This process follows diffusion-limited, sub-linear time dependence, often modeled with a t0.5 or t0.75 behavior, and is strongly influenced by temperature and SOC. The exponent 0.5 typically represents purely diffusion-controlled SEI growth, where lithium-ion transport through the SEI layer limits the reaction rate, as described by Fickian diffusion. The exponent 0.75 reflects a mixed diffusion–reaction regime, where both diffusion and interfacial reaction kinetics contribute, often observed in long-term storage due to SEI densification and porosity changes.22,23 In this study, the exponent 0.75 was chosen to align with experimental observations of sub-linear SEI growth in LiFePO4/graphite cells under prolonged storage, as validated against data from Sui et al.24
In addition to these insights, long-term empirical studies have provided valuable data on how cell chemistry and electrode composition influence calendar aging. Frie et al.25 conducted a comprehensive five-year investigation into the calendar aging behavior of nickel-rich NCA 18650 cells equipped with silicon/graphite (Si/C) composite anodes. The study revealed that elevated storage voltages significantly accelerated degradation, primarily due to intensified solid electrolyte interphase (SEI) growth on the high-surface-area silicon particles. This resulted in a marked increase in capacity fade compared to graphite-only systems, underscoring the vulnerability of silicon-rich anodes to interfacial instability during prolonged storage. Similarly, Wang et al.26 examined the calendar aging of lithium titanate (LTO)-based cells (renowned for their structural stability) under high-temperature storage conditions (60 °C) at different SOC levels. The findings indicated that even in LTO systems, higher SOCs (particularly 100%) led to greater capacity loss and impedance growth, driven by enhanced side reactions and SEI formation at the electrode/electrolyte interface.
Therefore, accurate modeling of calendar aging (especially SEI dynamics and electrolyte conductivity degradation) is crucial for battery lifetime prediction, health estimation, and thermal/storage management in modern battery applications.
Understanding the influence of storage conditions, such as temperature and duration, on calendar aging is essential for predicting battery lifetime and optimizing battery management strategies. Elevated temperatures significantly accelerate degradation reactions, which can lead to rapid performance deterioration if not properly managed.27 Experimental investigations have shown that even moderate increases in storage temperature can markedly reduce battery lifespan.28
Physics-based modeling frameworks (particularly pseudo-two-dimensional (P2D) models) have demonstrated significant efficacy in capturing the intricate interplay among electrochemical, thermal, and aging phenomena in lithium-ion batteries (LIBs).14,29 In contrast to empirical or data-driven approaches, P2D models are grounded in fundamental transport and reaction equations, allowing for the resolution of spatial and temporal distributions of critical internal state variables. These include lithium-ion concentration gradients within electrodes and electrolyte phases, local overpotentials, and temperature fields across the cell architecture. This level of detail enables more accurate and predictive assessments of performance degradation, especially under diverse storage and operational scenarios.30 When augmented with sub-models that represent aging mechanisms (such as solid electrolyte interphase (SEI) layer growth, lithium plating, and active material loss) P2D frameworks become powerful tools for simulating long-term calendar aging behavior. They provide deeper mechanistic insight into capacity fade and resistance growth, which are influenced by electrochemical and thermal inhomogeneities at the micro- and macro-scale.8 Consequently, these models serve not only as diagnostic platforms but also as predictive engines for optimizing cell design, storage protocols, and battery management strategies aimed at extending LIB lifespan. Recent advancements in P2D modeling have significantly enhanced the ability to predict lithium-ion battery aging under diverse conditions. For instance, Wickramanayake et al.31 developed a novel solver for a P2D electrochemical-thermal aging model, incorporating kinetic- and diffusion-limited SEI growth models to estimate capacity fade in real-time with less than 1% error compared to commercial solvers, validated across multiple operational scenarios including 1C discharge/charge cycles. Similarly, Di Prima et al.32 introduced a pseudo-four-dimensional (P4D) model for cylindrical NMC cells, extending the P2D framework to account for 3D electrochemical and thermal non-uniformities, revealing the impact of SEI growth and cathode electrolyte interface (CEI) formation on calendar aging at 60 °C. Additionally, Su et al.33 employed a P2D-based electrochemical-thermal coupling model to study capacity fade in M1254S2 button-type lithium-ion batteries, demonstrating that larger anode particle radii lead to thicker SEI films and increased resistance, exacerbating capacity loss over 3000 cycles. These studies demonstrate the robustness of P2D and extended models in capturing electrochemical and thermal dynamics, providing a strong foundation for analyzing calendar aging under varied storage scenarios.
This study aims to implement a physics-based simulation framework using the Pseudo-Two-Dimensional (P2D) model to investigate calendar aging phenomena in commercial LiFePO4/graphite cells under various temperature and SOC conditions. By incorporating SEI layer growth and electrolyte conductivity loss into the model, and validating it against long-term experimental data, the proposed approach enables a mechanistic understanding of degradation trends and provides predictive insights for optimizing storage strategies and battery management systems.
To maintain stable environmental conditions throughout the aging process, all cells were stored in precision climate chambers (Weiss WT3-180/40), capable of regulating the ambient temperature within a tolerance of ±0.5 °C. Each group of three cells was subjected to one of the five predefined storage conditions (Table 1). The storage tests extended over duration of up to 43 months for Cases 1–3, and 27 months for Cases 4–5, ensuring sufficient time to observe capacity fade well beyond the conventional end-of-life (EOL) threshold of 20%.
Case | Temperature | SOC level |
---|---|---|
Case 1 | 55 °C | 50% |
Case 2 | 47.5 °C | 50% |
Case 3 | 40 °C | 50% |
Case 4 | 55 °C | 10% |
Case 5 | 55 °C | 90% |
To quantify the degradation in capacity, reference measurements were performed at regular intervals of 30 days. Prior to each measurement, the cells were equilibrated to room temperature (25 °C). Discharge tests were conducted following the protocol described in Sui et al.,24 where cells were discharged at a constant current of 2.5 A (1C rate) to 2.0 V, charged at 1C to 3.6 V, and discharged again at 1C to 2.0 V. The capacity from the final discharge step was recorded as the remaining capacity for retention calculations. While a lower rate (e.g., C/3) with constant-current constant-voltage (CC–CV) charging is often preferred for precise capacity measurements in LiFePO4/graphite cells, the 1C-rate protocol was adopted to align with the experimental conditions of Sui et al.,24 ensuring consistency for model validation.
The geometric configuration used in the simulation represents a typical cylindrical 2.5 Ah LiFePO4/C cell, consistent with the experimental setup described in ref. 24. The electrode stack included a 50 μm thick graphite anode, a 25 μm separator, and a 70 μm LiFePO4 cathode. Relevant material properties were taken from the same reference and supporting literature sources (Table 2).
Initial ionic conductivity | κ0 = 10 mS cm−1 |
Particle radius (anode) | rp = 5 μm |
Diffusion coefficient (anode) | Ds = 3.9 × 10−14 m2 s−1 |
Particle radius (cathode) | rp = 0.1 μm |
Diffusion coefficient (cathode) | Ds = 1.0 × 10−15 m2 s−1 |
Separator porosity | ε = 0.4 |
Separator tortuosity | τ = 2.5 |
Initial lithium concentrations were set to reflect specific SOC values of 10%, 50%, and 90%, and were held constant during the simulations to emulate open-circuit storage conditions. Boundary conditions included zero-flux (Neumann) conditions at the electrode-current collector interfaces.
Item | Value |
---|---|
Type | Cylindrical |
Dimensions | Ø 26 × 65 mm |
Weight | 76 g |
Nominal capacity | 2.5 Ah |
Nominal voltage | 3.3 V |
Maximum voltage | 3.6 V |
Minimum voltage | 2.0 V |
Maximum-continuous charge current | 10 A (4C) |
Maximum-continuous discharge current | 50 A (20C) |
Storage temperature | −40 °C to 60 °C |
Electrolyte | 1 M LiPF6 in EC![]() ![]() ![]() ![]() |
Anode | Graphite |
Cathode | LiFePO4 |
The spatial domain was one-dimensional across the thickness of the cell (from anode current collector to cathode current collector), while each electrode domain included a spherical radial coordinate to simulate intra-particle diffusion. The general structure of the governing equations is described in Table 4.
Description | Equation | Boundary conditions | |
---|---|---|---|
Electrolyte phase transport | ![]() |
∇ce·n|x=0,L = 0 | (1) |
Solid phase transport | ![]() |
![]() |
(2) |
Charge conservation (electrolyte) | ∇·(κeff∇ϕe) = −jli | ∇ϕe·n|x=0,L = 0 | (3) |
Charge conservation (solid) | ∇·(σeff∇ϕs) = jli | ![]() |
(4) |
Electrode kinetics (Butler–Volmer equation) | ![]() |
(5) | |
Reaction rate constant | ![]() |
Eact,k,neg = 20![]() |
(6) |
Eact,k,pos = 30![]() |
|||
Liquid phase diffusion coefficient (diffusivity) | ![]() |
Similar to ref. 38 and 39 | (7) |
Solid phase diffusion coefficient | ![]() |
Eact,Ds,neg = 68![]() |
(8) |
![]() |
Eact,Ds,pos = 3415 [J mol−1] | ||
Es,0,pos = 8.7 × 10−14 [m2 s−1] | |||
Es,0,neg = 1.52 × 10−12 [m2 s−1] (ref. 38 and 39) |
In the P2D simulation framework, the time-dependent electrolyte conductivity κ(t) is modeled as a function of temperature T, state of charge (SOC), and storage duration t, incorporating both empirical aging observations and physical degradation trends. The degradation behavior follows a power-law dependence on time and an Arrhenius-type dependence on temperature, reflecting the thermally activated nature of the decomposition reactions. Additionally, a linear dependence on SOC is included to capture the role of higher electrode potentials in promoting side reactions.
The conductivity evolution is modeled by the following expression:
![]() | (9) |
This model captures the experimentally observed trends in which conductivity declines faster at elevated temperatures and higher SOC levels due to increased rates of solvent oxidation, salt decomposition, and gas evolution reactions.42–44 The exponent n = 0.75 is consistent with prior works that report non-linear, self-limiting degradation kinetics under storage conditions, particularly in systems dominated by SEI-related and electrolyte side reactions.45
By integrating this expression into the P2D model, the simulation accurately reflects the time-dependent transport limitations in the electrolyte, which in turn influence lithium-ion diffusion, overpotentials, and ultimately the rate of SEI growth and capacity fade.
In the context of calendar aging, where the battery remains at open-circuit voltage and a fixed state of charge, SEI formation proceeds at a slow but steady rate due to persistent side reactions between the anode and the electrolyte. This process is thermally activated and strongly influenced by the state of charge (SOC), as higher SOCs correspond to lower anode potentials, which promote further reduction of electrolyte species.48,49
The SEI growth rate is linked to the side reaction current density jSEI, which is governed by a Tafel-type expression derived from Butler–Volmer kinetics:
![]() | (10) |
The cumulative growth of the SEI thickness over time is calculated from the integrated SEI formation charge:40
![]() | (11) |
![]() | (12) |
In this model, the SEI growth is directly linked to the loss of lithium inventory and hence to the capacity fade observed in long-term storage, as the lithium consumed in forming SEI is irreversibly lost from the electrochemically active pool. This coupling allows accurate prediction of capacity degradation as a function of time, temperature, and SOC within the P2D simulation environment.
![]() | (13) |
To elucidate the capacity fade model, the experimentally measured capacity during 1C-rate discharge tests, as reported in Sui et al.,24 is acknowledged to encompass both irreversible Loss of Lithium Inventory (LLI) and power fade resulting from increased internal resistance. Eqn (13) quantifies capacity fade predominantly through the charge consumed by SEI formation (QSEI), reflecting the primary LLI mechanism in calendar aging of LiFePO4/graphite cells. However, the P2D framework comprehensively accounts for kinetic limitations associated with power fade by dynamically simulating SEI thickness growth, which elevates interfacial resistance at the anode, and electrolyte conductivity loss, which increases transport resistance across the cell. These resistance-enhancing effects are fully integrated into the physics-based simulation, influencing lithium-ion transport and reaction kinetics. The robust agreement between simulated and experimental capacity fade, evidenced by RMSE values below 0.9 across all aging conditions (Fig. 1), validates the model's ability to capture the combined effects of LLI and power fade. By emphasizing LLI in eqn (13), the model provides a precise metric for the dominant irreversible degradation mechanism, while the P2D framework ensures a holistic representation of all pertinent degradation processes.
Fig. 1 compares simulated and experimental capacity fade for Cases 1–5, showing strong agreement with RMSE values of 0.8049 (Case 1), 0.8813 (Case 2), 0.3648 (Case 3), 0.4665 (Case 4), and 0.3413 (Case 5). Cases 1–3 (50% SOC at 40 °C, 47.5 °C, 55 °C) exhibit a clear trend of increasing capacity fade with temperature, consistent with Arrhenius-driven acceleration of SEI growth and electrolyte decomposition. Case 4 (10% SOC, 55 °C) shows the least fade, reflecting reduced side reaction rates at lower SOC. However, Case 5 (90% SOC, 55 °C) initially shows higher capacity fade than Case 1 (50% SOC, 40 °C) but falls below it after approximately 15 months. This behavior, observed in the experimental data, may result from the self-limiting nature of SEI growth at high SOC, where the thicker SEI layer (Fig. 3, 308.6 nm at 36 months) reduces further electrolyte reduction, slowing fade relative to temperature-driven degradation at 50% SOC. Additionally, experimental variations in cell preconditioning or SEI stabilization at high SOC could contribute to this trend.24 These results reinforce the nonlinear interplay of temperature and SOC, supporting the model's predictive capability.
The experimental data from Sui et al.24 utilized storage temperatures of 40 °C, 47.5 °C, and 55 °C to accelerate calendar aging, reflecting conditions relevant to high-stress applications. For model validation, simulations were conducted at these temperatures, achieving RMSE values below 0.9 (Fig. 1), confirming robust agreement with experimental capacity fade. Additionally, simulations at 25 °C were included as a reference condition to evaluate degradation under ambient storage, a scenario of practical interest for battery management but not covered in the experimental dataset.24 The 25 °C simulations extrapolate the validated model parameters (Tables 2–4) using Arrhenius and Tafel kinetics, providing predictive insights into low-temperature aging behavior, as shown in Fig. 2–5. This extension enhances the model's applicability across a broader range of storage conditions.
The pronounced increase in electrolyte conductivity loss at elevated temperatures is consistent with prior studies on thermally accelerated degradation in lithium-ion battery systems.50 As evidenced by the simulation results presented in Fig. 2, the rate of conductivity decline exhibits a clear exponential dependence on temperature. For instance, after 36 months of storage, conductivity loss at 55 °C reaches up to 22.86% at 90% SOC, compared to only 2.5% under ambient temperature (25 °C) and 10% SOC. This marked disparity highlights the critical role of thermal energy in promoting side reactions such as solvent decomposition, salt breakdown, and formation of resistive byproducts, which collectively hinder ionic transport through the electrolyte. In addition to temperature effects, the influence of state of charge (SOC) is shown to be equally significant. The data reveal that higher SOC levels (particularly above 50%) amplify conductivity loss across all temperature conditions. This behavior can be attributed to increased anode lithiation, which lowers the anode potential and accelerates the oxidative decomposition of the electrolyte.51 At 40 °C, for example, conductivity loss after 36 months increases from 5.29% at 10% SOC to 10.80% at 90% SOC, underscoring the compounding nature of SOC and temperature as degradation drivers.
The inclusion of 25 °C in simulations provides critical insights into calendar aging under ambient conditions, where degradation is significantly slower (e.g., 2.5% conductivity loss and 38 nm SEI thickness at 10% SOC after 36 months, Fig. 2 and 3). These results, derived from the validated P2D model, suggest that storage at ambient temperatures can substantially extend battery lifespan, supporting optimized storage strategies for applications with prolonged idle periods.24 These results support the hypothesis that storage at elevated SOC and temperature synergistically exacerbates electrolyte decomposition and transport limitation mechanisms. The combined effects of thermally activated kinetics and high electrode potentials accelerate the accumulation of inert species within the electrolyte, thereby impeding lithium-ion mobility and contributing to long-term performance degradation. This underscores the importance of thermal and SOC management during storage to mitigate calendar aging and extend battery life.
This exponential growth aligns with the diffusion-limited nature of SEI formation, where the rate of layer buildup is governed by both reaction kinetics and the gradual penetration of electrolyte decomposition products through the existing SEI. Elevated temperatures significantly enhance reaction rates at the anode/electrolyte interface, thereby intensifying SEI growth. Likewise, high SOC levels (particularly above 50%) correspond to lower anode potentials, which promote continuous electrolyte reduction and facilitate further SEI formation.
The compounding effect of temperature and SOC is evident when comparing mid- and high-SOC conditions. At 55 °C, SEI thickness at 50% SOC reaches 223.7 nm after 36 months, while increasing SOC to 90% accelerates SEI growth by over 38%, culminating in a 308.6 nm layer. These results support prior findings that elevated SOC enhances the availability of electrochemical driving forces for parasitic side reactions, thereby intensifying irreversible lithium consumption and capacity loss.
The SEI growth data substantiate the underlying model assumptions, including the adoption of a sub-linear (t0.75) time dependence and Arrhenius-type temperature scaling. This modeling framework effectively captures the self-limiting yet thermally activated nature of SEI formation, which is essential for predicting long-term lithium inventory loss under varied storage scenarios.
The simulation results presented in Fig. 2 and 3 highlight a strong correlation between the rate of SEI growth and the magnitude of electrolyte conductivity loss. For instance, under the most aggressive aging condition (55 °C, 90% SOC), the SEI thickness increases to 308.6 nm after 36 months, while the corresponding conductivity loss reaches 22.86% (Fig. 4). In contrast, at 25 °C and 10% SOC, both metrics remain relatively low, with SEI thickness limited to 38.0 nm and conductivity loss to 2.5%. This trend indicates that as SEI formation accelerates, it not only depletes active lithium but also exacerbates electrolyte degradation, thereby reducing transport efficiency across the cell.
These findings suggest that SEI and electrolyte degradation are not independent phenomena but rather co-evolving and self-reinforcing processes. From a practical standpoint, this interdependence has significant implications for long-term battery storage strategies. Minimizing SEI formation through optimized SOC and thermal control not only preserves lithium inventory but also mitigates electrolyte transport degradation, thereby maintaining cell impedance within acceptable limits.
This behavior can be attributed to the electrochemical potential of the anode, which decreases as SOC increases, thereby enhancing the thermodynamic driving force for electrolyte reduction reactions. As a result, a more aggressive SEI formation occurs, leading to faster lithium consumption and a thicker, more resistive interphase layer. Moreover, higher SOC levels typically correspond to a higher degree of lithiation in the graphite anode, where the electrolyte is more prone to reductive decomposition due to the lower interfacial potential.
The data from the current study (Fig. 5) reinforce the necessity of accounting for SOC-induced asymmetry in predictive battery aging models. For instance, the consistently higher SEI growth rates at 90% SOC across all time intervals suggest that time-averaged degradation estimates may significantly underestimate localized capacity loss in real-world scenarios, particularly for applications where batteries are regularly stored or operated at high charge levels.
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