中国机械工程

• 机械基础工程 • 上一篇    下一篇

基于免疫算法的四驱插电式混合动力汽车控制策略多目标优化

王永宽1,2;钱立军1;牛礼民2   

  1. 1.合肥工业大学汽车与交通工程学院,合肥,230009
    2.安徽工业大学机械工程学院,马鞍山,243032
  • 出版日期:2017-07-25 发布日期:2017-07-26
  • 基金资助:
    国家自然科学基金资助项目(51275002);
    2012年国家新能源汽车技术创新工程资助项目(财建[2012]1095)
    National Natural Science Foundation of China (No. 51275002)

Multi-objective Optimization of Control Strategies for Four-wheel Drive PHEV Based on Immune Algorithm

WANG Yongkuan1,2;QIAN Lijun1;NIU Limin2   

  1. 1.School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei,230009
    2.School of Mechanical Engineering,Anhui University of Technology,Ma'anshan,Anhui,243032
  • Online:2017-07-25 Published:2017-07-26
  • Supported by:
    National Natural Science Foundation of China (No. 51275002)

摘要: 为改善四驱插电式混合动力汽车的燃油经济性和排放性能,构建了基于规则的能量管理控制策略,并建立了整车仿真模型;将多种群协同进化的思想引入免疫算法,提出多种群免疫算法,并运用该算法对四驱插电式混合动力汽车的控制策略进行了多目标优化;在dSPACE实时仿真系统上对优化前后的控制策略进行了硬件在环仿真实验。结果表明:优化后的控制策略控制效果良好,且发动机的燃油消耗降低了12.71%,HC、CO以及NOx的排放分别下降了15.74%、15.92%和12.69%。

关键词: 插电式混合动力汽车, 控制策略, 优化, 实时仿真

Abstract: To improve fuel economy and emission performance of a four-wheel drive PHEV, the rule-based energy management control strategy and vehicle simulation model were established firstly. Then, a multi-population immune algorithm was presented by introducing the concept of multi-population co-evolution. The algorithm was adopted to optimize the control strategies of four-wheel drive PHEV with multiple objectives. Finally, a hardware-in-the-loop simulation experiment was carried out on dSPACE real-time simulation system to test the control strategy before and after optimizations. Experimental results show that the optimized control strategy may achieve good control effectiveness, furthermore, the fuel consumption of engine is reduced by 12.71% while the HC, CO and NOx emissions are decreased by 15.74%, 15.92% and 12.69% respectively.

Key words: plug-in hybrid electric vehicle(PHEV), control strategy, optimization, real time simulation

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