扩展卡尔曼滤波器
计算机科学
控制理论(社会学)
健康状况
锂离子电池
集合卡尔曼滤波器
作者
Jinhao Meng,Guangzhao Luo,Fei Gao
出处
期刊:IEEE Transactions on Power Electronics
[Institute of Electrical and Electronics Engineers]
日期:2016-03-01
卷期号:31 (3): 2226-2238
被引量:158
标识
DOI:10.1109/tpel.2015.2439578
摘要
An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.
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