吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 1-7.doi: 10.13229/j.cnki.jdxbgxb201701001

• 论文 •    下一篇

插电式混合动力汽车动力传动系参数优化

王庆年1, 段本明1, 王鹏宇1, 拱印生2, 朱庆林2   

  1. 1.吉林大学 汽车仿真与控制国家重点实验室,长春130022;
    2.启明信息技术股份有限公司 电子服务中心,长春130122
  • 收稿日期:2015-11-10 出版日期:2017-01-20 发布日期:2017-01-20
  • 通讯作者: 王鹏宇(1979-),男,副教授,博士.研究方向:节能与新能源汽车.E-mail:wangpy@jlu.edu.cn
  • 作者简介:王庆年(1952-),男,教授,博士生导师.研究方向:节能与新能源汽车.E-mail:wqn@jlu.edu.cn
  • 基金资助:

    “863”国家高技术研究发展计划项目(W65-BK-2012-0007).

Optimization of powertrain transmission parameters of plug-in hybrid electric vehicle

WANG Qing-nian1, DUAN Ben-ming1, WANG Peng-yu1, GONG Yin-sheng2, ZHU Qing-lin2   

  1. 1.State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China;
    2.Electric Service Center,Qiming Information Technology Co.,Ltd.,Changchun 130122,China
  • Received:2015-11-10 Online:2017-01-20 Published:2017-01-20

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摘要：

在既定的动力总成功率参数及整车控制策略前提下,为充分挖掘插电式混合动力汽车(PHEV)节能潜力,以满足整车性能为约束条件,以百公里行驶成本为响应,利用最优拉丁超立方设计方法探索了其传动系统所有因子的空间响应特性,辨别了系统关键设计因子,给出了最优局部区域。基于此,建立了系统径向基(RBF)神经网络模型,并充分利用非线性二次规划算法较强的局部优化能力,在上述局部区域内得到了传动系参数全局最优组合以及对应的百公里行驶成本。结果显示:基于近似模型的优化方法精度较高,误差为1.06%;百公里行驶成本降低了9.72%。

关键词: 车辆工程, 插电式混合动力汽车, 百公里行驶成本, 最优拉丁超立方设计, 径向基神经网络模型, 非线性二次规划算法

Abstract:

In order to fully explore the energy saving potential of plug-in hybrid electric vehicle with determined power parameters of powertrain and control strategy, and in meeting the vehicle performance constraints, the running cost of 100 km was proposed as the response. The optimal Latin hypercube design was used to investigate the spatial response characteristics of all factors of the transmission system, based on which the key design factors of the system were identified and the optimal local area was confirmed. Combined with making full use of the advantages of strong local optimization ability of nonlinear quadratic programming algorithm, the Radial Basis (RBF) neural network model was established to obtain global optimal solution and the best response of the optimization problem. The model error is 1.06% which shows that the optimization method based on approximation model is of high accuracy. The running cost of 100 km is reduced by 9.72%.

Key words: vehicle engineering, plug-in hybrid electric vehicle(PHEV), running costs of 100 km, optimal latin hypercube design, RBF neural network, nonlinear quadratic programming algorithm

中图分类号: 

  • U469.7
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