A lightweight two-stage physics-informed neural network for SOH estimation of lithium-ion batteries with different chemistries ☆

Accurately estimating the battery state of health (SOH) is essential for ensuring the safe and reliable operation of battery systems of electric vehicles. However, due to the complex and variable operating conditions encountered in practical applications, achieving precise and physics-informed SOH estimation remains challenging. To address these problems, this paper develops a lightweight two-stage physics- informed neural network (TSPINN) method for SOH estimation of lithium-ion batteries with different chemistries. Specifically, this paper utilizes firstly relaxation voltage data obtained after a full charge to determine the aging-related parameters of physical equivalent circuit model (ECM). Additionally, incremental capacity (IC) feature is extracted by analyzing peak values of the IC curve during the charging phase, which thereby constitutes the first stage of the proposed TSPINN, termed as physics-informed data augmentation for SOH estimation. Additionally, the physical information can be further embedded by incorporating feature knowledge related to mechanisms into the loss function, and ultimately, the second stage of the proposed TSPINN is developed, which is named the physics-informed loss function. The effectiveness of the TSPINN method was confirmed through the experimental data for LiNi0.86Co0.11Al0.03O2 (NCA) and LiNi0.83Co0.11Mn0.07O2 (NCM) battery materials under different temperature conditions. The final experimental results indicate that the TSPINN method achieved SOH estimation with a mean absolute error (MAE) of 0.675%, showing improvements of approximately 29.3%, 60.3%, and 8.1% compared to methods using only ECM, IC, and integrated features, respectively. The results validate the effectiveness and adaptability of TSPINN, establishing it as a reliable solution for advanced battery management systems. (c) 2025 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.