Prediction of Lithium Battery Health State Based on Temperature Rate of Change and Incremental Capacity Change

Tao Zhang, Yang Wang, Rui Ma, Yi Zhao, Mengjiao Shi () and Wen Qu ()
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Tao Zhang: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Yang Wang: College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
Rui Ma: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Yi Zhao: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China
Mengjiao Shi: Key Laboratory of Bio-Based Material Science and Technology, Ministry of Education, Northeast Forestry University, Harbin 150040, China
Wen Qu: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

Energies, 2023, vol. 16, issue 22, 1-17

Abstract: With the use of Li-ion batteries, Li-ion batteries will experience unavoidable aging, which can cause battery safety issues, performance degradation, and inaccurate SOC estimation, so it is necessary to predict the state of health (SOH) of Li-ion batteries. Existing methods for Li-ion battery state of health assessment mainly focus on parameters such as constant voltage charging time, constant current charging time, and discharging time, with little consideration of the impact of changes in Li-ion battery temperature on the state of health of Li-ion batteries. In this paper, a new prediction method for Li-ion battery health state based on the surface difference temperature (DT), incremental capacity analysis (ICA), and differential voltage analysis (DVA) is proposed. Five health factors are extracted from each of the three curves as input features to the model, respectively, and the weights, thresholds, and number of hidden layers of the Elman neural network are optimized using the Whale of a Whale Algorithm (WOA), which results in an average decrease of 43%, 49%, and 46% in MAE, RMSE, and MAPE compared to the Elman neural network. For the problem where the three predictions depend on different sources, the features of the three curves are fused using the weighted average method and predicted using the WOA–Elman neural network, whose MAE, RMSE, and MAPE are 0.00054, 0.0007897, and 0.06547% on average. The results show that the proposed method has an overall error of less than 2% in SOH prediction, improves the accuracy and robustness of the overall SOH estimation, and reduces the computational burden to some extent.

Keywords: state of health of lithium batteries; incremental capacity analysis; difference temperature; differential voltage analysis; Elman (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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