Predicting battery lifetime under varying usage conditions from early aging data

Accurate battery lifetime prediction is important for maintenance, warranties, and cell design. However, manufacturing variability and usage -dependent degradation make life prediction challenging. Here, we investigate new features derived from capacityvoltage data in early life to predict the lifetime of cells cycled under varying charge rates, discharge rates, and depths of discharge. The early -life features capture a cell's state of health and the change rate of component -level degradation modes. Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite lithium -ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in -distribution cells with 15.1% mean absolute percentage error (MAPE). A hierarchical Bayesian model shows improved performance on extrapolation, achieving 21.8% MAPE for out -of -distribution cells. Our approach highlights the importance of using domain knowledge of battery degradation to inform feature engineering and model construction. Further, a new publicly available battery lifelong aging dataset is provided.