吴慕遥
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影响因子：5.6
DOI码：10.1109/TIM.2024.3390162
发表刊物：IEEE Transactions on Instrumentation and Measurement
关键字：Lithium-ion power battery, Equivalent circuit modelNoiseinformation covariance matching, Improved adaptive unscented kalman filter, Multi-time-scale
摘要：Accurate estimation of the State of Charge (SOC) in lithium-ion power batteries is crucial for ensuring battery reliability, optimizing energy management strategies, enhancing battery efficiency, and prolonging battery service life. To account for the diverse time-varying characteristics of both State of Charge (SOC) and model parameters in lithium-ion power batteries, this paper introduces a multi-time scale improved adaptive unscented Kalman filter. An improved method for adaptive updating of the noise covariance matrix is introduced, aiming to bolster algorithm convergence, diminish filtering result oscillations, guarantee the positivity of the system process noise covariance matrix, and ensure system response speed. Then, a multi-time-scale SOC estimation approach is introduced under micro, medium and macroscopic three different time-scale. The experimental findings indicate that even though the initial SOC estimation value and the initial maximum available capacity estimation value are both wrong, the SOC estimation results maintain an accuracy level below 1.00% for mean absolute error, 1.00% for root mean square error and 1.50% for maximum absolute error under DST and FUDS operational modes, except for the extreme temperature 0°C which rarely appears in actual scenes. Moreover, it also can reach the accurate terminal voltage estimation results, the reasonable maximum available capacity and the polarization voltage estimation results. Meanwhile, it can effectively reduce the battery management system calculation burden by using micro-medium-macroscopic three different time-scale.
备注：中科院二区Top
合写作者：Li Wang,Yuqing Wang
第一作者：Muyao Wu
论文类型：期刊论文
通讯作者：Ji Wu
学科门类：工学
文献类型：J
卷号：73
是否译文：否
发表时间：2024-04-17
收录刊物：SCI
