Calibration methodology for energy management system of a plug-in hybrid electric vehicle. (15th March 2017)

• Record Type: Journal Article
• Authors: Duan, Benming; Wang, Qingnian; Zeng, Xiaohua; Gong, Yinsheng; Song, Dafeng; Wang, Junnian
• Is Part Of: Energy conversion and management. Volume 136(2017)
• Page Start: 240
• Page End: 248
• Publication Date: 2017-03-15
• Subjects: Calibration -- Energy management system -- Hybrid electric vehicle -- Radar chart -- Optimal Latin hypercube design -- RBF neural network; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2016.12.068
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Calibration theory of EMS is proposed. A comprehensive evaluating indicator is constructed by radar chart method. Optimal Latin hypercube design algorithm is introduced to obtain training data. An approximation model is established by using a RBF neural network. Offline calibration methodology improves the actual calibration efficiency. Abstract: This paper presents a new analytical calibration method for energy management strategy designed for a plug-in hybrid electric vehicle. This method improves the actual calibration efficiency to reach a compromise among the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economic performance is constructed by using a radar chart method. A radial basis functions (RBFs) neural network model is proposed to establish a precise model among control parameters and the comprehensive evaluation indicator. The optimal Latin hypercube design is introduced to obtain the experimental data to train the RBFs neural network model. And multi-island genetic algorithm is used to solve the optimization model. Finally, an offline calibration example is conducted. Results validate the effectiveness of the proposed calibration approach in improving vehicle performance and calibration efficiency.