Lithium battery SOH estimation based on improved GWO-SVR

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Lithium battery SOH estimation based on improved GWO-SVR
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                        Liang Yang1Liang Yang
Collegee of Electromechanical Engineering, Central South University of Forestry and Technology, Changsha 410000, China
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Zhou Yongjun1Zhou Yongjun
Collegee of Electromechanical Engineering, Central South University of Forestry and Technology, Changsha 410000, China
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Jiang Shuxia1Jiang Shuxia
Collegee of Electromechanical Engineering, Central South University of Forestry and Technology, Changsha 410000, China
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Yuan Xiaowen2Yuan Xiaowen
Guizhou MeiLing Power Sources Co., Ltd., Zunyi 563000, China
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Zhang Song1Zhang Song
Collegee of Electromechanical Engineering, Central South University of Forestry and Technology, Changsha 410000, China
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Affiliation:1.Collegee of Electromechanical Engineering, Central South University of Forestry and Technology, Changsha 410000, China; 2.Guizhou MeiLing Power Sources Co., Ltd., Zunyi 563000, China
Clc Number:TM912
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Abstract:

In order to improve the estimation accuracy of lithium battery state of health, a lithium battery SOH estimation method based on IGWO-SVR is proposed. Firstly, aiming at the problem of kernel parameter selection of support vector regression (SVR), the improved gray wolf (IGWO) algorithm is used to optimize the kernel parameters of support vector regression (SVR). The SVR estimation model realizes the estimation of the SOH of lithium batteries. Based on the NASA battery dataset, the model is trained and validated and compared with the SVR and GWO-SVR methods. The results show that the IGWO-SVR method can effectively improve the accuracy and stability of SOH estimation, and the maximum estimation error does not exceed 2%.

Key words:Lithium battery;health status;support vector regression;improved grey wolf algorithm

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Online: February 18,2024
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