Artificial Intelligence Approaches to Battery Health Assessment: Opportunities, Challenges and Future Directions

Abstract

Accurate estimation of the state of health and remaining useful life of lithium-ion batteries is essential for ensuring the
reliability, safety and longevity of electric vehicles, stationary storage systems and portable electronics. Traditional approaches based on electrochemical diagnostics, equivalent circuit models and reduced-order physics-based methods provide valuable mechanistic insights but face significant limitations under dynamic real-world operating conditions. Recent advances in artificial intelligence have transformed battery diagnostics by enabling data-driven extraction of degradation-sensitive features from voltage, current and temperature measurements. Machine learning algorithms such as random forests and support vector regression demonstrate strong state of health estimation accuracy when combined with engineered features, while deep learning models, including convolutional neural networks, long short-term memory, convolution long short-term memory and attention-based architectures, achieve state-of the-art performance by learning nonlinear temporal patterns directly from raw time-series data. Hybrid physics-informed neural networks further enhance interpretability and generalization by embedding electrochemical constraints into model architectures. In addition to supervised learning, reinforcement learning has emerged as a promising method for adaptive battery management, enabling real-time optimization of charging strategies, thermal control and power allocation to minimize degradation and extend battery lifetime. When integrated into digital twin frameworks, artificial intelligence models support continuous, real-time state of health/remaining useful life tracking and predictive maintenance across large battery fleets. Despite these advances, challenges remain in data availability, domain shift, model interpretability, computational constraints and the absence of standardized validation protocols. Future research will focus on physics-informed hybrid artificial intelligence, transfer learning for cross-chemistry generalization, federated learning for privacy-preserving fleet deployment and standardized benchmarking frameworks. Together, these developments signal the emergence of next-generation intelligent battery management systems that combine accurate health estimation with adaptive, degradation-aware control.