Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network

Xin Zhang (), Jiawei Hou, Zekun Wang and Yueqiu Jiang
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Xin Zhang: School of Automobile and Traffic, Shenyang Ligong University, Shenyang 110159, China
Jiawei Hou: School of Automobile and Traffic, Shenyang Ligong University, Shenyang 110159, China
Zekun Wang: School of Automobile and Traffic, Shenyang Ligong University, Shenyang 110159, China
Yueqiu Jiang: School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China

Energies, 2022, vol. 16, issue 1, 1-17

Abstract: The traditional ampere-hour (Ah) integration method ignores the influence of battery health (SOH) and considers that the battery capacity will not change over time. To solve the above problem, we proposed a joint SOH-SOC estimation model based on the GWO-BP neural network to optimize the Ah integration method. The method completed SOH estimation through the GWO-BP neural network and introduced SOH into the Ah integration method to correct battery capacity and improve the accuracy of state of charge (SOC) estimation. In addition, the method also predicted the SOH of the battery, so the driver could have a clearer understanding of the battery aging level. In this paper, the stability of the joint SOH-SOC estimation model was verified by using different battery data from different sources. Comparative experimental results showed that the estimation error of the joint SOH-SOC estimation model could be stabilized within 5%, which was smaller compared with the traditional ampere integration method.

Keywords: joint SOH-SOC estimation; GWO-BP; Ah integration method; battery management systems (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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