Hybrid neural network-based state of health estimation for lithium-ion batteries in electric vehicles

Abstract

Lithium-ion batteries fundamentally influence the reliability, safety, and service life of electric vehicles, making accurate State of Health (SOH) estimation essential for advanced battery management systems. To address the nonlinear, multiscale, and usage-dependent degradation patterns found in real-world cycling, this study proposes a hybrid neural network framework that combines convolutional feature extraction with recurrent temporal modeling. The architecture incorporates mathematically defined convolutional modules for localized voltage–capacity pattern recognition, recurrent units for longterm degradation dynamics, and a fusion mechanism supported by multi-metric loss functions. Using publicly available datasets—including National Aeronautics and Space Administration (NASA) cells B0005/B0006 and the Oxford Battery Degradation Dataset—the method is evaluated under diverse degradation trajectories characterized by mid-life plateaus, nonlinear capacity fade, and accelerated end-of-life decline. Results demonstrate that the hybrid model exhibits improved robustness to measurement noise, partial cycling, and operational variability compared with single-path architectures, thereby providing more stable and adaptable SOH predictions. These findings indicate that hybrid neural network frameworks offer a promising technical pathway for next-generation battery management systems, enabling more reliable SOH monitoring, enhanced predictive maintenance strategies, and longer-lasting electric vehicle battery performance.