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基于多目标遗传算法的混合电动汽车参数优化 被引量：15

Parameters Optimization of Hybrid Electric Vehicle Based on Multi-objective Genetic Algorithms
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摘要 动力系统和控制器参数的同时优化是提高混合电动汽车(HEV)燃油经济性并降低排放的关键。这类优化问题涉及多个相互冲突的优化目标和非线性约束,是典型的多目标优化问题。文中采用多目标遗传算法求解该优化问题的Pareto最优解集,并应用ADVISOR对实际算例的优化结果进行比较分析。结果表明,应用该方法可找到多组可行解,在满足原车动力性要求的前提下能有效提高燃油经济性,降低排放。 Concurrent optimization for parameters of powertrain and control system is the key to improving fuel economy and reducing emission of hybrid electric vehicle. It involves several conflicting optimization objectives and nonlinear constraints, and so is a typical multi-objective optimization problem. In this paper, the multi-objec- tive genetic algorithms are used to find the Pareto-optimal solution set, and the ADVISOR is utilized to evaluate the results of optimization for a real vehicle. The results demonstrate that the proposed approach can find many feasible solutions to improve fuel economy and reduce emission without worsening power performance.
作者 房立存 秦世引
机构地区 北京航空航天大学自动化科学与电气工程学院
出处 《汽车工程》 EI CSCD 北大核心 2007年第12期1036-1040,共5页 Automotive Engineering
关键词 混合电动汽车 多目标遗传算法 多目标优化 PARETO最优解集 HEV Multi-objective genetic algorithms Multi-objective optimization Pareto-opthnal set
分类号 U469.72 [机械工程—车辆工程]
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参考文献18

  • 1Gao W Z, Porandla S K. Design Optimization of a Parallel Hybrid Electrlc Powertrain[ C ]. IEEE Conference on Vehicle Power and Propulslon,2005.
  • 2Gakdi V, Ippolite L, Piccolo A, et al. Evaluation of Emissions Influence on Hybrid Electric Vehicles Sizing[ C]. Proc. of International Conference on Power Electronics, Electrical Drives, Advanced Machines and Power Quality(SPEEDAM) ,2000.
  • 3Zhu Y, Chen Y b, Tian G Y, et al. A Four-Step Method to Design an Energy Management Strategy for Hybrid Vehicles[ C ]. Proceeding of the 2004 American Control Conference,2004.
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  • 5Wipke K, Markel T. Optimization Techniques for Hybrid Electric Vehicle Analysis Using ADVISOR[C]. Proceedings of ASME, International Mechanical Engineering Congress and Exposition,2001.
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  • 7浦金欢,殷承良,张建武.遗传算法在混合动力汽车控制策略优化中的应用[J].中国机械工程,2005,16(7):648-652. 被引量：37
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二级参考文献10

  • 1Kimura A, Abe T, Sasaki S. Drive Force Control of a Parallel-series Hybrid System. JSAE Review, 1999, 20(3): 337～341.
  • 2Paganelli G, Ercole G, Brahma A, et al. General Supervisory Control Policy for the Energy Optimization of Charge-sustaining Hybrid Electric Vehicles. JSAE Review, 2001, 22(4):511～518.
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  • 4Piccolo A, Ippolito L, Galdi V, et al. Optimization of Energy Flow Management in Hybrid Electric Vehicles via Genetic Algorithms.IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, Como, Italy, 2001.
  • 5Markel T, Wipke K. Optimization Techniques for Hybrid Electric Vehicle Analysis Using ADVISOR.ASME Int. Mechanical Engineering Congress and Exposition, New York, 2001.
  • 6Wipke K, Markel T, Nelson D. Optimizing Control Strategy and Degree of Hybridization for a Hydrogen Fuel Cell SUV. The 18th International Electric Vehicle Symposium, Berlin,2001.
  • 7Kita H, Ono I, Kobayashi S. Multi-parental Extension of the Unimodal Normal Distribution Crossover for Real-coded Genetic Algorithms.1999 Congress on Evolutionary Computation, Washington, D.C.,1999.
  • 8Satoh H, Yamamura M, Kobayashi S. Minimal Generation Gap Model for GAs Considering both Exploration and Exploitation.Methodologies for the Conception, Design, and Application of Intelligent Systems, Proc. of the 4th International Conference on Soft Computing (Iizuka'96), Iizuka, Fukuoka, Japan,1996.
  • 9Deb K. An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering, 2003, 186(2): 311～338.
  • 10Markel T, Brooker A, Hendricks T, et al. ADVISOR: a Systems Analysis Tool for Advanced Vehicle Modeling. Journal of Power Sources, 2002, 110(2): 255～266.

共引文献36

同被引文献97

引证文献15

二级引证文献128

汽车工程
2007年 第12期

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