Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks

The paper presents a method for predicting the remaining useful life of lithium‑ion batteries using only the first few charge‑discharge cycles. It transforms voltage, current, and capacity data into time‑frequency representations via continuous wavelet transform, compresses the resulting scalograms to reduce redundancy, and feeds them into deep neural networks. Experiments on eight benchmark datasets show the approach outperforms previous methods across diverse chemistries and cycling protocols, and visual explanations confirm the model focuses on meaningful time‑frequency regions.