AI‐Driven Battery Management for E‐Mobility: Predicting State of Health and Remaining Useful Life

Predicting the State of Health (SoH) and Remaining Useful Life (RUL) is crucial in lithium‐ion batteries (LiBs) used in electric vehicles (EVs). The rapid growth of EVs has created a strong demand for an advanced battery management system (BMS). The BMS plays a critical role in maintaining battery health, safety, and efficiency, which are the heart of EV technology. By monitoring and controlling battery parameters, BMS can optimize charging, prevent degradation, and mitigate risks, providing a reliable EV experience. To address the limitations of conventional methods in capturing complex degradation dynamics, this paper proposes a novel hybrid deep learning framework, TransLSTM, which integrates long short‐term memory (LSTM) networks with a Modified Transformer Encoder. Extensive testing with the National Aeronautics and Space Administration (NASA) Ames Prognostics Centre of Excellence (PCoE) Battery Dataset demonstrates the superior accuracy of our method, surpassing traditional BMS approaches. The LSTM network with the Modified Transformer Encoder (TransLSTM) model achieved exceptional performance, with a root mean squared error (RMSE) of 0.0354%, mean squared error (MSE) of 0.0012%, and mean absolute error (MAE) of 0.0209% on test datasets, demonstrating significant advancements in predictive accuracy and reliability. This research highlights the potential of artificial intelligence (AI) to revolutionize EV battery management, leading to improved efficiency, reliability, and cost‐effectiveness.