基于WPT与SSA优化的数据驱动电池健康状态估计OA
Data-driven Battery Health State Estimation Based on WPT and SSA Optimization
锂电池的健康状态SOH(state of health)会随着充放电次数增多而逐渐降低,其衰退特征隐藏在电池电流、电压等物理信息中.针对锂电池SOH在线估计提出了一种基于小波包变换WPT(wavelet packet transform)和麻雀搜索算法 SSA(sparrow search algorithm)的数据驱动方法,并提取了放电电压平台时间 DVPT(discharge voltage plateau time)和恒流充电时间CCCT(constant current charging time)的特征指标,在马里兰大学电池公开数据集进行算法验证.研究结果表明:所提出的DVPT特征指标与SOH具有强相关性,在4个电池中的平均皮尔逊相关系数为98.38%;模型预测最优结果的RMSE为0.019 1、MAE为0.012 5、R2为97.78%.
The state of health(SOH)of Li-ion batteries gradually decreases with the number of charges and discharges,whose features are hidden in the physical information such as battery current and voltage.A data-driven method based on wavelet packet transform(WPT)and sparrow search algorithm(SSA)is proposed for online estimation of SOH,and the feature indices of discharge voltage plateau time(DVPT)and constant current charging time(CCCT)are proposed.The algorithm is verified on the University of Maryland battery public dataset.The results show that the proposed DVPT characteristic metric has a strong correlation with SOH,with an average Pearson correlation coefficient of 98.38%across the four batteries;the model predicted the optimal results with RMSE of 0.019 1,MAE of 0.012 5,and R2 of 97.78%.
黄杰明;黄小荣;张庆波;林炜;吴树平;罗俊杰
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信息技术与安全科学
锂电池健康状态数据驱动麻雀搜索算法
Lithium batterystate of healthdata-drivensparrow search algorithm
《电源学报》 2026 (5)
256-264,9
南方电网公司科技资助项目(031900KK52220011)This work is supported by Science and Technology Program of China Southern Power Grid under the grant 031900KK52220011
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