电气传动2026，Vol.56Issue(9)：64-74,11.DOI:10.19457/j.1001-2095.dqcd27039
基于混合健康因子和改进BiGRU神经网络的锂电池SOH估计
State of Health Estimation of Lithium-ion Batteries Based on Health Indicators and Improved BiGRU Neural Network
摘要
Abstract
Accurate estimation of the state of health(SOH)of lithium-ion batteries is crucial for enhancing their safety and extending their service life.To address the limitations of existing data-driven methods in capturing health indicators and achieving sufficient model generalization,an SOH estimation method based on hybrid degradation features and an improved bidirectional gated recurrent unit(BiGRU)neural network was proposed.First,by integrating charge-discharge curve features with parameters of a second-order equivalent circuit model,a multidimensional hybrid health indicator was constructed to comprehensively characterize battery degradation behavior.Then,an improved Harris Hawks optimization(IHHO)algorithm combining chaotic initialization,nonlinear decreasing factor,and Cauchy-Gaussian hybrid mutation was employed to optimize the BiGRU hyperparameters.Meanwhile,a self-attention(SA)mechanism was incorporated to build the IHHO-BiGRU-SA model.Finally,the proposed model was validated using CALCE and NASA battery datasets.Experimental results show that compared with traditional methods,the proposed IHHO-BiGRU-SA model averagely reduces the mean absolute error(MAE)and root mean square error(RMSE)by 46.0%and 43.9%on the CALCE dataset,and by 39.3%and 38.7%on the NASA dataset,respectively.These results demonstrate that the proposed method achieves high estimation accuracy and excellent generalization performance in SOH estimation.关键词
锂离子电池/健康状态估计/改进双向门控循环单元/混合健康因子Key words
lithium-ion battery/state of health(SOH)estimation/improved bidirectional gated recurrent unit(BiGRU)/hybrid health indicator分类
信息技术与安全科学引用本文复制引用
陈将宏,杨昊,杨志淳,闵怀东..基于混合健康因子和改进BiGRU神经网络的锂电池SOH估计[J].电气传动,2026,56(9):64-74,11.基金项目
国家重点研发计划政府间国际科技创新合作项目(2024YFE0115600) （2024YFE0115600）
国网总部科技项目(52150025000Q) （52150025000Q）
