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Lithium-ion batteries are critical to electric vehicles (EVs) but degrade over time, requiring accurate State of Health (SOH) and Remaining Useful Life (RUL) estimation. This review examines recent AI-based methods, especially Convolutional and Recurrent Neural Networks , for their effectiveness in prediction. It discusses key optimization strategies such as feature selection, parameter tuning, and transfer learning . Public datasets (NASA, CALCE, Oxford) are evaluated for benchmarking. The paper also assesses model complexity, performance metrics, and deployment challenges. Finally, it outlines future directions for improving battery management systems , supporting more efficient, reliable, and scalable integration into real-world EV applications.
Nazim et al. (Wed,) studied this question.