带状态检测机制的ELM-UKF算法估计锂电池SOC策略  

Strategy for Estimation of Lithium-Battery SOC by ELM-UKF Algorithm with State Detection Mechanism

作　　者：谈发明[1] 赵俊杰[1] Tan Faming;Zhao Junjie(Jiang Su University of Technology,Changzhou 213001)

机构地区：[1]江苏理工学院,常州213001

出　　处：《汽车技术》2025年第2期46-54,共9页Automobile Technology

基　　金：国家自然科学基金青年科学基金项目(61803186);江苏省工程师学会重点研究课题(JSIE2024ZD06)。

摘　　要：为解决无迹卡尔曼滤波(UKF)算法对锂电池荷电状态(SOC)估计精度不高的问题,结合极限学习机(ELM)与UKF间的互补优势,提出了一种带状态检测机制的ELM-UKF组合算法估计锂电池SOC。首先,算法利用UKF估计电池SOC的相关滤波数据作为样本集训练ELM模型,将训练成功的ELM模型用于在线补偿UKF的SOC估计误差,进而实现估计偏差的实时修正;其次,算法针对ELM模型预测输出设计了状态检测机制,以此减小ELM模型预测输出过拟合对SOC估计波形平滑度的影响。试验结果表明,相较于单一类型的算法,所提出的组合算法具有良好的鲁棒性和泛化性,能有效提升锂电池SOC的估计效果。To address the issue of low accuracy in estimating the State of Charge(SOC)of lithium batteries using the Unscented Kalman Filter(UKF)algorithm,a combined ELM-UKF algorithm with a state detection mechanism is proposed,leveraging the complementary advantages of Extreme Learning Machine(ELM)and UKF for estimating the SOC of lithium batteries.Firstly,the algorithm uses the relevant filtering data estimated by UKF for battery SOC as a sample set to train the ELM model.The successfully trained ELM model is then used to online compensate for the SOC estimation error of UKF,thereby achieving real-time correction of estimation deviations.Secondly,the algorithm designs a state detection mechanism for the predictive output of the ELM model to reduce the impact of overfitting in the ELM model’s predictive output on the smoothness of the SOC estimation waveform.Experimental results show that,compared to single-type algorithms,the proposed combined algorithm exhibits good robustness and generalization,effectively enhancing the estimation performance of lithium battery SOC.

关 键 词：荷电状态 无迹卡尔曼滤波 极限学习机 状态检测 精度 

分 类 号：TM912[电气工程—电力电子与电力传动]