A Generative Adversarial Network and Feed-Forward Neural Network Approach to Predict Health of Electric Vehicle Lithium-Ion Batteries in Extreme Temperature

Abstract

he growing adoption of electric vehicles (EVs) has intensified the need for better battery management systems (BMS) to ensure the longevity, efficiency, and safety of lithium-ion batteries (LiBs). Temperature fluctuations have significant effects on the State of Health (SOH) of LiBs, but real-world datasets that encompass diverse and extreme thermal circumstances are limited, resulting making in precise SOH prediction an ongoing challenge. This study presents a novel predictive modeling approach that combines Generative Adversarial Networks (GANs) with Feedforward Neural Networks (FFNNs). The GAN generates realistic synthetic battery data over wide temperature ranges, followed by post-processing techniques to correlate synthetic results with actual battery behavior. This combined dataset, comprising both real and synthetic data, is subsequently used to train an FFNN model for accurate SOH prediction. The key contributions are: 1) development of a GAN-based data augmentation pipeline to address data scarcity under extreme temperatures, 2) integrating synthetic and real data using some post processing techniques for improved model reliability and generalization, and 3) implementation of an FFNN-based SOH predictor that attains high predictive precision with minimal computational cost suitable for resource-constrained BMS. The proposed GAN–FFNN model attains an R2 of 0.94 and an MAE of 0.29 between −30° C and 60° C, demonstrating superior performance and revealing that high temperatures (>40° C) accelerate degradation more than low temperatures. This is the first known GAN-augmented framework for SOH prediction across such an extensive temperature range.