Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle
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- 作者：B. M. Duan ; Q. N. Wang ; J. N. Wang ; X. N. Li…
- 关键词：Key WordsCalibration ; Plug ; in hybrid electric vehicle ; Radar chart ; Optimal Latin hypercube design ; RBF neural network
- 刊名：International Journal of Automotive Technology
- 出版年：2017
- 出版时间：April 2017
- 年：2017
- 卷：18
- 期：2
- 页码：335-344
- 全文大小：
- 刊物类别：Engineering
- 刊物主题：Automotive Engineering;
- 出版者：The Korean Society of Automotive Engineers
- ISSN：1976-3832
- 卷排序：18
文摘
This paper presents a calibration method of a rule-based energy management strategy designed for a plug-in hybrid electric vehicle, which aims to find the optimal set of control parameters to compromise within the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economy performance is constructed by the method of radar chart. Moreover, a radial basis functions (RBFs) neural network model is proposed to establish a precise model within the control parameters and the comprehensive evaluation indicator. The best set of control parameters under offline calibration is gained by the multi-island genetic algorithm. Finally, the offline calibration results are compared with the experimental results using a chassis dynamometer. The comparison results validate the effectiveness of the proposed offline calibrating approach, which is based on the radar chart method and the RBF neural network model on vehicle performance improvement and calibrating efficiency.