Calibration methodology for energy management system of a plug-in hybrid electric vehicle

被引：35

作者：

Duan, Benming ^{[1
]}

Wang, Qingnian ^{[1
]}

Zeng, Xiaohua ^{[1
]}

Gong, Yinsheng ^{[2
]}

Song, Dafeng ^{[1
]}

Wang, Junnian ^{[1
]}

机构：

[1] Jilin Univ, State Key Lab Automot Simulat & Control, No 5988,Renmin Ave, Changchun 130025, Peoples R China

[2] Qiming Informat Technol Co Ltd, Elect Serv Ctr, Changchun 130122, Peoples R China

来源：

ENERGY CONVERSION AND MANAGEMENT | 2017年 / 136卷

基金：

中国国家自然科学基金;

关键词：

Calibration; Energy management system; Hybrid electric vehicle; Radar chart; Optimal Latin hypercube design; RBF neural network; CONTROL STRATEGIES; URBAN ROADS; OPTIMIZATION; DESIGN;

D O I：

10.1016/j.enconman.2016.12.068

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

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.(C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：240 / 248

页数：9

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