EVALUATION OF LITHIUM-ION BATTERY STATE OF HEALTH BASED ON IMPROVED HHO-SVR ALGORITHM

Wu Jian; Jin Hui; Ge Hongjuan; Gong Qi; Chang Qi; Zhao Jiayi

doi:10.19912/j.0254-0096.tynxb.2024-0638

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 91-100. DOI: 10.19912/j.0254-0096.tynxb.2024-0638

EVALUATION OF LITHIUM-ION BATTERY STATE OF HEALTH BASED ON IMPROVED HHO-SVR ALGORITHM

Wu Jian¹, Jin Hui¹, Ge Hongjuan¹, Gong Qi², Chang Qi³, Zhao Jiayi¹

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Abstract

This study extracts health characteristics of lithium batteries from charge and discharge data with incremental capacity analysis to predict the capacity of lithium-ion batteries. Meanwhile, a method for estimating the state of health of lithium-ion batteries is proposed based on the improved Harris hawk optimization algorithm （IHHO） and support vector regression （SVR）. To address the problem that the Harris hawk optimization algorithm （HHO） tends to stagnate at local optima, the IHHO algorithm introduces a pooling mechanism and a migrating search strategy based on the HHO algorithm, while modifying the position update equation to prevent premature convergence. Based on comparisons using the CEC2017 test suite, the optimization capabilities of IHHO and HHO algorithms are evaluated. This shows that the IHHO algorithm has a higher convergence speed and optimization accuracy. For some test functions, the optimization accuracy of the IHHO algorithm is improved by more than three orders of magnitude, effectively avoiding premature convergence. In addition, the comparative experiments are conducted on the NASA lithium-ion battery dataset to optimize the standard SVR with IHHO and HHO algorithms. The results show that IHHO-SVR significantly improves the prediction accuracy of the state of health, reducing the root mean square error by more than 40% compared to the HHO-SVR. In addition, comparisons with other literature models indicate the superior performance of the IHHO-SVR model. For certain prediction results, the root mean square error of IHHO-SVR is reduced by at least 15%.

Key words

lithium-ion battery / state of health / capacity increment analysis / improved Harris hawk optimization algorithm / support vector regression

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Wu Jian, Jin Hui, Ge Hongjuan, Gong Qi, Chang Qi, Zhao Jiayi. EVALUATION OF LITHIUM-ION BATTERY STATE OF HEALTH BASED ON IMPROVED HHO-SVR ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 91-100 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0638

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