文章基本信息

标题：State of Charge Estimation Accuracy in Charge Sustainable Mode of Hybrid Electric Vehicles
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作者：Imene Mansour ; Erik Frisk ; Adel Jemni 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：2158-2163
DOI：10.1016/j.ifacol.2017.08.274
语种：English
出版社：Elsevier
摘要：AbstractThe charge sustaining mode of a hybrid electric vehicle maintains the state of charge of the battery within a predetermined narrow band. Due to the poor system observability in this range, the state of charge estimation is tricky, and inadequate prior knowledge of the system uncertainties could lead to deterioration and divergence of estimates. In this paper, a comparative study of three estimators tuned based on the noise covariance matching technique is established in order to analyze their robustness in the state of charge estimation. Simulation results show a significant enhancement of filter accuracy using this adaptation. The adaptive particle filter has the best estimation results but it is vulnerable to model parameter uncertainties, further it is time consuming. On the other hand, the adaptive Unscented Kalman filter and the adaptive Extended Kalman filter show enough estimation accuracy, robustness for model uncertainty, and simplicity of implementation.
关键词：KeywordsState of charge estimationhybrid electric vehicleadaptive Extended Kalman filteradaptive Unscented Kalman filteradaptive particle filternoise covariance matrix tuning