Battery cycle life study through relaxation and forecasting the lifetime via machine learning

Battery lifetime modeling and prediction of precise capacity degradation for real-life applications are critical to understanding the complex and non-linear battery behavior. However, the application of accurate and robust aging models on dynamic on-road scenarios is still a challenge. In this work, a comprehensive aging dataset of 40 Nickel Manganese Cobalt (NMC) cells is generated for two years considering distinct relaxation phases in the function of the state of charge (SoC), temperature, and time. A qualitative analysis of the diversified aging parameters along with the sensitivity analysis of the rest criteria is conducted. Taking the discharge capacity as the pivotal predictor, a robust training dataset is built and preliminary fed to common data-driven models. Among them, the Gaussian process regression (GPR) is identified to be the best suit with which a 0.02% root-meansquared error (RMSE) can be achieved for battery life prediction when tested with a static profile choosing an exponential kernel. Further, to demonstrate a real-life scenario, a worldwide harmonized light-duty test cycle (WLTC) is performed, and the capacity fade percentile can be predicted accurately with a 0.05% RMSE. This research shows that data-driven algorithms like GPR can be a promising online tool that can forecast the entire lifetime with high precision for dynamic profiles.