Evaluation of the State of Health of Lithium-Ion Battery Based on the Temporal Convolution Network

Abstract

The state of health (SOH) of lithium-ion batteries is an important part of the battery management system (BMS). Accurately grasping the SOH of the lithium-ion battery will help replace the battery in time, to avoid accidents. Aiming at the problems of complex BMS management and high calculation cost caused by too many inputs/attributes, this study used feature engineering to mine the higher temperature variety rate associated with degraded capacity as the input of temporal convolutional networks (TCNs) and SOH as the output to establish the TCN model. On this basis, three lithium-ion batteries, namely, as B0005, B0007, and B0018 are verified, and the mean absolute error (MAE) and root mean square error (RMSE) of predicted SOH are not more than 1.455% and 1.800%, respectively. To further obtain the uncertain expression of predicted SOH, this study adopts the sampling method to obtain the confidence interval of lithium-ion battery SOH prediction results.