Deep Learning-Driven Prediction of Lithium-Ion Battery Life Cycle Using Discharge Cycle Analysis

Accurate prediction of the life cycle of lithium-ion batteries is essential for applications like electric vehicles (EVs), energy storage, and consumer electronics. Traditional physics-based models face limitations due to the complex nature of battery degradation, variability in operating conditions, and manufacturing differences. This research introduces a deep learning method to predict the battery life cycle of fast-charging lithium-ion batteries. A 2D Convolutional Neural Network (CNN) models the relationship between sensor measurements, such as voltage, temperature, discharge capacity, and battery cycle life. Experimental data from 40 batteries, with cycle lives ranging from 150 to 2300 cycles, is used for training and testing. Proposed CNN method achieved a root mean squared error (RMSE) of around 37.20 cycles and an average percentage error of 13.84 % on the test set, demonstrating that deep learning models can effectively predict battery life by learning complex patterns in sensor data. The approach offers a scalable and accurate method for cycle life estimation, significantly improving battery management systems and enabling real-time health monitoring.