Accurate and interpretable state of health estimation of lithium-ion battery based on multimodal laser-induced plasma spectroscopic sensing

Abstract

Lithium-ion batteries serve as a core component for next-generation power systems and electrical vehicles. As the key factor of the battery management system (BMS), state of health (SOH) significantly affects battery operational performance, making its accurate estimation crucial. Existing methods struggle to simultaneously balance efficiency, generalization, and interpretability. The significant quantitative correlation exists between the lithium content in the anode and the SOH. By capturing laser spectroscopic signals and monitoring the lithium content, accurate estimation and reliable prediction of SOH can be achieved. In this study, a novel multimodal laser spectroscopy sensing method is proposed, which achieves the confirmation of battery aging mechanisms, accurate estimation of SOH, and reliable prediction of service life. Specifically, the content of lithium elements is accurately monitored by collecting spectral signals from the battery anode, while ultrasonic and image information supplement additional detection insights. This integration significantly enhances the accuracy and precision of battery state estimation. A dedicated multimodal laser spectroscopic sensing network (MLSS-Net) is developed to fuse these multimodal features, enabling precise SOH estimation and reliable prediction of battery remaining lifespan. Validation experiments demonstrate the efficacy of the proposed method. This work offers a dependable technical solution for Li-ion battery SOH evaluation.