Evaluation of the State of Health of Lithium-Ion Battery Based on the Temporal Convolution Network - Zhang, Dan; Zhao, Weihua; Wang, Long; Chang, Xucheng; Li, Xiang; Wu, Peng (Frontiers in Energy Research, 2022)
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 to 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 paper uses feature engineering to mine the higher temperature variety rate associated with degraded capacity as the input of temporal convolutional networks (TCN) and SOH as the output, to establish the TCN model. On this basis, three lithium-ion batteries such as B0005, B0007, and B0018 are verified, and the mean absolute error (MAE) and root mean error (RMSE) of predicted SOH is no more than 1.455% and 1.800% respectively. To further obtain the uncertain expression of predicted SOH, this paper adopts the sampling method to obtain the confidence interval of lithium-ion battery SOH prediction results.