Aging trajectory prediction of lithium-ion batteries based on mechanical-electrical features via nonlinear autoregressive and regression neural networks

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The prediction of the lifespan of lithium-ion batteries (LIBs) is crucial for gaining early insights into battery degradation, enabling the optimization of battery usage and management strategies. This work assesses the expansion and aging characteristics of LIBs under various operating conditions and develops a combined machine-learning method to predict the aging trajectory of LIBs based on mechanical and electrical features. To avoid errors caused by secondary data processing, five mechanical-electrical features are directly extracted from the stress and electrochemical characteristic curves to reflect the battery aging state. A nonlinear autoregressive (NAR) neural network is adopted to predict the change trend of aging features (AFs) using historical data. Subsequently, the nonlinear regression (NR) neural network is employed for aging trajectory prediction using the aging feature dataset, which consists of both historical and predicted data. The experimental results demonstrate that the proposed method can ensure that the mean absolute error (MAE) in aging trajectory prediction is within 0.25 %. The method comprehensively analyzes and utilizes the mechanical-electrical properties of LIBs to enhance the predictive accuracy, expanding the scope of the predictive model in practical applications.