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汽车安全与节能学报

汽车安全与节能学报 ›› 2015, Vol. 6 ›› Issue (03): 265-271.DOI: 10.3969/j.issn.1674-8484.2015.03.010

• 汽车节能与环保 • 上一篇    下一篇

考虑电驱动系统成本的混合动力汽车参数综合优化

周云山，杨豪杰   

  1. 湖南大学 汽车电子与控制技术教育部工程研究中心，长沙 410082，中国
  • 收稿日期:2015-05-06 出版日期:2015-09-25 发布日期:2015-10-12
  • 作者简介:第一作者 / First author ： 周云山（1957 －），男（汉），湖南，教授。E-mail: zys_8888@sina.com 第二作者 / Second author ： 杨豪杰，硕士研究生。E-mail: yanghaojie_hnu@163.com
  • 基金资助:

    国家自然科学基金项目（51175156）

Synthetic parameter optimization of hybrid electric vehicle considering electric drive system cost

ZHOU Yunshan, YANG Haojie   

  1. Engineering Research Center of Automotive Electronics and Control Technology of Ministry of Education, Hunan University, Changsha 410082, China
  • Received:2015-05-06 Online:2015-09-25 Published:2015-10-12

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摘要：

提出了一种并联式混合动力汽车(HEV) 参数综合优化算法，以解决其能量管理与动力系统
匹配经常各自独立进行的问题。该方法考虑电驱动系统成本，用改进型模糊能量管理策略，以能量
管理策略参数、动力系统匹配参数为决策变量，以等效综合油耗、电机与电池组总成本为目标函数，
在ADVISOR 仿真环境下，用多目标遗传算法优化求解。结果表明：在保证整车动力性的前提下优化
后，等效油耗降低23.0%，电机和电池组总成本降低41.9% ；一氧化碳CO 的100 km 排放质量降低
10.8%， 碳氢化合物HC 的排放降低22.2%，氮氧化物NOx 的排放降低27.0%，改善了发动机效率与电
机效率；验证了该方法的有效性。

关键词: 混合动力汽车, 成本模型, 参数综合优化, 模糊能量管理策略, 多目标遗传算法

Abstract:

A synthetic parameter optimization method of hybrid electric vehicle (HEV) was proposed to solve
the problem that energy management and matching of power system are usually conducted respectively.
Considering the cost of electric drive system and using an improved fuzzy energy management strategy, the
parameters of energy management strategy and the parameters of power system were taken as decision
variables; meanwhile the equivalent synthetic fuel consumption and the cost of motor and battery pack were
taken as objective functions. Then under the simulation environment of ADVISOR, a multi-objective genetic
algorithm was used to find the optimal solution. Simulation results show that under the constrains of vehicle
dynamic quality, the equal fuel consumption per 100km is reduced by 23.0%, the cost of motor and battery pack
is reduced by 41.9%, the CO emissions per kilometer is reduced by 10.8%, the HC emissions per kilometer is
reduced by 22.2%, the NOx emissions per kilometer is reduced by 27.0%, as well as the efficiency of engine and
motor has been improved. These results verify the effectiveness of the method.

Key words: hybrid electric vehicle, cost model, synthetic parameter optimization, fuzzy energy management strategy, multi-objective genetic algorithm