CHEN Yang,HUANG Jiang-dong,YU Chun-lei,XIE Ji,JIANG Wei.Online Rapid Detection of Lithium-ion Battery State of Health Based on IWOA-SVR[J].Journal of Instrumental Analysis,2025,44(03):402-410.
CHEN Yang,HUANG Jiang-dong,YU Chun-lei,XIE Ji,JIANG Wei.Online Rapid Detection of Lithium-ion Battery State of Health Based on IWOA-SVR[J].Journal of Instrumental Analysis,2025,44(03):402-410. DOI: 10.12452/j.fxcsxb.240712214.
Online Rapid Detection of Lithium-ion Battery State of Health Based on IWOA-SVR
In this paper,a detection and evaluation method of lithium-ion battery state of health(SOH) is proposed by integrating the improved whale optimization algorithm and support vector regression(IWOA-SVR). Firstly,the charging/discharging data under different charging/discharging strategies are collected,and the critical battery aging characteristic parameters are extracted. Then Pearson correlation analysis is applied to verify the strong correlation between the parameters and the SOH,and the algorithm integrates the adaptive weight adjustment mechanism and Levy flight strategy into the traditional whale optimization algorithm,which effectively overcomes the problem of the large error of the traditional method when evaluating the SOH online. Finally,in order to verify the effectiveness of the algorithm,experimental test data under two typical operating conditions of constant-current and constant-voltage charging and constant-current charging are used for validation,and the results show that the IWOA-SVR detection method has higher stability and accuracy,and the maximal error can be controlled within 1.4%. Meanwhile,IWOA-SVR significantly outperforms the comparison algorithms in two key evaluation metrics,namely,mean absolute percentage error(MAPE) and root mean square error(RMSE),which fully proves its high accuracy and strong robustness in the online detection of SOH in lithium-ion batteries.
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