Lithium-ion battery charge-discharge cycle forecasting using LSTM neural networks - Srikantappa, Vimala Channapatana; Devarakonda, Seshachalam (International Journal of Power Electronics and Drive Systems (IJPEDS), 2025)
An important component for the dependable and safe utilization of lithium-ion batteries is the ability to accurately and efficiently predict their remaining useful life (RUL). In this research, a long short-term memory recurrent neural network (LSTM RNN) model is trained to learn from sequential data on discharge capacities across different cycles and voltages. The model is also designed to function as a cycle life predictor for battery cells that have been cycled under varying conditions. By leveraging experimental data from the NASA battery dataset, the model achieves a promising level of prediction accuracy on test sets consisting of approximately 200 samples.