Abstract
Accurate multi-state estimation of lithium-ion batteries (LIBs) is essential for electric vehicle (EV) battery management systems. Existing electrochemical models face challenges in parameter calibration, while purely data-driven methods lack physical interpretability. To address these limitations, this study proposes an integrated framework combining a pseudo-two-dimensional (P2D) electrochemical model with a generative adversarial network-long short-term memory (GAN-LSTM) architecture. A hybrid simulated annealing-particle swarm optimization (SA-PSO) algorithm was developed for non-invasive parameter calibration of the Tesla Model S battery P2D model, achieving a mean absolute error (MAE) of 0.027 V in terminal voltage prediction during 1C constant-current discharge. The calibrated model, integrated with vehicle dynamics simulations, generated physics-based multivariate time-series data across diverse operational scenarios. These data were utilized to train the GAN-LSTM framework, which synergizes LSTM’s temporal modeling with GAN’s adversarial training for robust state estimation. Experimental results demonstrate the framework’s high accuracy, with determination coefficients (R2) of 0.9965 for state of charge (SOC) and 0.9843 for state of health (SOH). This work establishes a novel methodology that bridges electrochemical mechanisms with data-driven modeling, providing a physics-informed solution for multi-state battery estimation without relying on artificial feature engineering or unvalidated assumptions. The proposed framework offers practical value for next-generation battery management systems in real-world EV applications.
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