| 杨东晓,王贺,党宏宇,袁宇轩,和杰公.基于最优重构健康因子和RIME-SVR的锂电池健康状态估计研究[J].电子测量与仪器学报,2025,39(5):188-196 |
| 基于最优重构健康因子和RIME-SVR的锂电池健康状态估计研究 |
| Estimation of lithium battery health state based on optimalreconstructed health factor and RIME-SVR |
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| DOI： |
| 中文关键词: 锂电池健康状况 最优重构健康因子 霜冰优化算法 支持向量回归 CEEMDAN |
| 英文关键词:lithium battery health optimal reconstruction of health factors rime ice algorithm support vector regression CEEMDAN |
| 基金项目:中央高校基本业务费（BLX201405）项目资助 |
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| Author | Institution |
| Yang Dongxiao | School of Technology, Beijing Forestry University, Beijing 100083, China |
| Wang He | School of Technology, Beijing Forestry University, Beijing 100083, China |
| Dang Hongyu | School of Technology, Beijing Forestry University, Beijing 100083, China |
| Yuan Yuxuan | School of Technology, Beijing Forestry University, Beijing 100083, China |
| He Jiegong | School of Technology, Beijing Forestry University, Beijing 100083, China |
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| 中文摘要: |
| 为提高锂电池健康状态(SOH)估计精度，提出一种基于最优重构健康因子和霜冰算法优化支持向量回归(RIME SVR)相融合的估计方法。首先从锂电池充放电过程提取3个可测量健康因子，利用Pearson法分析验证其与SOH相关性；其次利用完备集合经验模态分解算法(CEEMDAN)对健康因子分解重构，通过实验验证法确定最优重构方式，有效降低数据噪声和容量回升现象对SOH估计干扰；最后搭建基于RIME算法优化的SVR估计模型。实验采用NASA电池退化数据，结果表明，相比于粒子群（PSO）和人工蜂群(ABC)优化算法，RIME优化SVR参数时表现出更快收敛速度和更强全局搜索能力，显著提升模型性能。此外，基于最优重构健康因子和RIME-SVR的锂电池SOH估计模型3项指标均优于对比实验中其他模型，具有更高的估计精度和拟合度。使用最优重构健康因子Dtv1+Ti1+Tdv1作为输入，模型平均绝对误差（MAE）、均方根误差（RMSE）分别低于0.37和0.55、R2高于0.92，表明所提方法具备良好的普适性和鲁棒性。 |
| 英文摘要: |
| In order to improve the estimation accuracy of lithium battery state of health (SOH), a novel estimation method combining the optimal reconstruction of Health Indicators and RIME-optimized support vector regression (RIME-SVR) is proposed. First, three measurable Health Indicators are extracted from the charging and discharging process of lithium batteries, and their correlation with SOH is verified using the Pearson method. Subsequently, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is employed to decompose and reconstruct the health indicators. The optimal reconstruction method is determined through experimental validation, effectively reducing the interference of data noise and capacity recovery on SOH estimation. Finally, an SVR estimation model optimized by the RIME algorithm is established. The experiments are conducted using NASA battery degradation data. The results show that compared with particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms, RIME exhibits faster convergence speed and stronger global search capability when optimizing SVR parameters, significantly enhancing model performance. Moreover, the lithium battery SOH estimation model based on the optimal reconstruction of health indicators and RIME-SVR outperforms other models in the comparative experiments in terms of three indicators, achieving higher estimation accuracy and fitting degree. When the optimally reconstructed health indicator Dtv1+Ti1+Tdv1 is used as input, the model’s average mean absolute error (MAE) is below 0.37, root mean squared error (RMSE) is below 0.55, and the coefficient of determination is higher than 0.92, indicating good universality and robustness. |
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