A Deep Learning Approach for Electric Vehicle Battery Capacity Degradation and Health Early Prediction of Battery Remaining Useful Life

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is crucial, in terms of optimizing battery performance, reducing maintenance costs, and assuring safety in applications such as electric vehicles (EV), energy storage systems, and consumer electronics. Existing RUL prediction methodologies are either physics-based approaches that assess high interpretability but require extensive domain expertise, or data-driven approaches which actually operate on machine learning and deep learning models but lack physical interpretability. Therefore, in this chapter the authors propose a “hybrid deep learning framework” that combines 1D convolutional neural networks (CNNs) with an attention mechanism to enhance feature extraction and prediction accuracy. Moreover, various alternative RUL prediction methods, such as support vector machines (SVMs), random forest regression, long short-term memory (LSTM), and transformer networks are explored in depth. The feature selection is enhanced using SelectKBest with mutual information regression (MIR), and model performance is evaluated through using mean squared error (MSE), mean absolute error (MAE), and also root mean squared error (RMSE). This study shows that the data-driven approach HybridCNN model, combined through advanced feature selection and regularization techniques, will enhance the generalization maintaining the computational efficiency, ensuring it will be applicable to multiple industries that mostly rely on battery-powered systems.