基于改进HHO-SVR算法的锂电池健康状态评估

吴健; 金辉; 葛红娟; 宫綦; 常琦; 赵佳怡

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

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基于改进HHO-SVR算法的锂电池健康状态评估

更新时间：2026-01-08

- 基于改进HHO-SVR算法的锂电池健康状态评估
- 基于改进HHO-SVR算法的锂电池健康状态评估
- 太阳能学报 2025年46卷第12期页码：91-100
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家自然科学基金民航联合基金重点资助项目（U2233205;U2133203）
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0638
  中图分类号： TM912
- 网络出版：2026-01-07，
  
  纸质出版：2025
- 稿件说明：
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吴健, 金辉, 葛红娟, 等. 基于改进HHO-SVR算法的锂电池健康状态评估[J]. 太阳能学报, 2025,46(12):91-100.

吴健, 金辉, 葛红娟, et al. 基于改进HHO-SVR算法的锂电池健康状态评估[J]. 2025, 46(12): 91-100.
吴健, 金辉, 葛红娟, 等. 基于改进HHO-SVR算法的锂电池健康状态评估[J]. 太阳能学报, 2025,46(12):91-100. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0638.

吴健, 金辉, 葛红娟, et al. 基于改进HHO-SVR算法的锂电池健康状态评估[J]. 2025, 46(12): 91-100. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0638.

摘要

在分析锂电池健康特征的基础上

提出一种基于改进哈里斯鹰优化算法（IHHO）和支持向量机回归（SVR）的锂电池健康状态评估方法;IHHO引入池化机制和迁移搜索策略以及螺旋位置更新策略

解决现有哈里斯鹰优化算法（HHO）的早熟收敛问题。基于CEC2017测试套件比较研究IHHO、HHO等算法的寻优能力

结果表明IHHO算法的收敛速度和寻优精度更好

在部分测试函数上寻优精度提升3个数量级以上

能有效避免早熟收敛。进一步

面向NASA锂电池数据集

采用IHHO算法对SVR的惩罚系数C、不敏感损失因子ε和核参数γ进行寻优

开展基于IHHO-SVR的锂电池健康状态评估实验

结果表明IHHO-SVR能有效提高锂电池健康状态的预测精度

相较于HHO-SVR

均方根误差降低40%以上;此外

将该文所构建的IHHO-SVR模型与其他文献进行对比

结果表明IHHO-SVR模型性能优越

在部分预测结果上

IHHO-SVR的预测均方根误差降低15%以上。

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%.

关键词

Keywords

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