Concurrent optimization for parameters of powertrain and control system of hybrid electric vehicle based on multi-objective genetic algorithms

被引:0
|
作者
Fang, Li-Cun [1 ]
Qin, Shi-Yin [1 ]
机构
[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100083, Peoples R China
来源
2006 SICE-ICASE INTERNATIONAL JOINT CONFERENCE, VOLS 1-13 | 2006年
关键词
concurrent optimization; hybrid electric vehicle(HEV); multi-objective genetic algorithms(MOGAs);
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The optimizing design of hybrid electric vehicle (HEV) aims at improving fuel economy and decreasing emissions subject to the satisfaction of its drivability. The concurrent optimization for main parameters of powertrain components and control system is the key to implement this objective. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Thus, it is necessary to employ an effective strategy and algorithms to solve this problem. hi this paper, an approach of optimization is developed based on the multi-objective genetic algorithms, which can realize the optimization to parameters of powertrain and control system simultaneously and find the Pareto-optimal solution set successfully subject to user-selectable performance constraints. This optimal parameter set provides a wide range of choices for the design, which can improve the fuel economy and reduce emissions without sacrificing vehicle performance. A case simulation is carried out and simulated by ADVISOR, the results demonstrate the effectiveness of the approach proposed in this paper.
引用
收藏
页码:67 / +
页数:3
相关论文
共 50 条
  • [21] Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems
    Alshammari, Nahar F.
    Samy, Mohamed Mahmoud
    Barakat, Shimaa
    MATHEMATICS, 2023, 11 (07)
  • [22] Multi-objective optimisation for battery electric vehicle powertrain topologies
    Othaganont, Pongpun
    Assadian, Francis
    Auger, Daniel J.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2017, 231 (08) : 1046 - 1065
  • [23] Multi-objective portfolio optimization utilizing hybrid genetic algorithms
    Jiang, Jiabao
    Xu, Wenbo
    DCABES 2006 PROCEEDINGS, VOLS 1 AND 2, 2006, : 482 - 487
  • [24] Multi-objective robust optimization design for powertrain mount system of electric vehicles
    Xin, Fu-Long
    Qian, Li-Jun
    Du, Hai-Ping
    Li, Wei-Hua
    JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL, 2017, 36 (03) : 243 - 260
  • [25] Multi-objective optimization of the suspension parameters in the in-wheel electric vehicle
    Li, Ruihua
    JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2021, 21 (04) : 1013 - 1020
  • [26] Multi-Objective optimization in battery selection for hybrid electric vehicle applications
    Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, India
    J. Electr. Syst., 2 (325-343):
  • [27] Multi-Objective Optimization in Battery Selection for Hybrid Electric Vehicle Applications
    Panday, Aishwarya
    Bansal, Hari Om
    JOURNAL OF ELECTRICAL SYSTEMS, 2016, 12 (02) : 325 - 343
  • [28] Multi-objective optimization design and control of plug-in hybrid electric vehicle powertrain for minimization of energy consumption, exhaust emissions and battery degradation
    da Silva, Samuel Filgueira
    Eckert, Jony Javorski
    Silva, Fabricio Leonardo
    Silva, Ludmila C. A.
    Dedini, Franco Giuseppe
    ENERGY CONVERSION AND MANAGEMENT, 2021, 234
  • [29] Design optimization of vehicle EHPS system based on multi-objective genetic algorithm
    Cui, Taowen
    Zhao, Wanzhong
    Wang, Chunyan
    ENERGY, 2019, 179 : 100 - 110
  • [30] Design Optimization of Vehicle EHPS System Based on Multi-objective Genetic Algorithm
    Cui, Taowen
    Zhao, Wanzhong
    Wang, Chunyan
    JOINT INTERNATIONAL CONFERENCE ON ENERGY, ECOLOGY AND ENVIRONMENT ICEEE 2018 AND ELECTRIC AND INTELLIGENT VEHICLES ICEIV 2018, 2018,