计算机集成制造系统 ›› 2020, Vol. 26 ›› Issue (5期): 1246-1256.DOI: 10.13196/j.cims.2020.05.010

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电动汽车再生制动过程换挡点多目标优化

李聪波,胡芮,朱道光,杨青山   

  1. 重庆大学机械传动国家重点实验室
  • 出版日期:2020-05-31 发布日期:2020-05-31
  • 基金资助:
    国家自然科学基金资助项目(51475059);教育部创新团队发展计划资助项目(IRT_15R64)。

Multi-objective optimization of gear decision for electric vehicle during regenerative braking

  • Online:2020-05-31 Published:2020-05-31
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.51475059),and the Innovation Research Team Development Program of the Ministry of Education,China(No.IRT_15R64).

摘要: 为了在提高制动能量回收效率的同时保证制动时整车的舒适性,针对两挡双离合式电动汽车,提出再生制动过程的换挡点多目标优化模型。介绍了电动汽车复合制动系统的结构,并分析了换挡对再生制动回收能量和制动时整车舒适性的影响;提出两挡双离合式电动汽车的再生制动能量回收策略框架,在此基础上根据模糊控制原理设计了输出为最大再生制动力分配系数的模糊识别器;建立了以最大再生制动力分配系数为约束条件,再生制动回收能量和制动时整车冲击度为优化目标的换挡点多目标优化模型;在新欧洲循环工况下进行仿真分析,结果表明与无换挡的能量回收策略相比,所提能量回收策略回收的制动能量提高了6.14%,同时换挡冲击度满足德国标准。

关键词: 电动汽车, 再生制动, 模糊控制, 换挡点, 多目标优化

Abstract: To improve the regenerative braking energy recovery rate and the comfort of vehicle,for electric vehicles fitted with dual clutch transmission,a multi-objective optimization model for gear decision during regenerative braking was proposed.The structure of hybrid electric braking system was introduced and the influence of shift on regenerative braking energy recovery and vehicle comfort was analyzed.The energy recovery strategy was proposed,and the fuzzy identifier was investigated according to the fuzzy control principle.The maximum regenerative braking force distribution coefficient was generated from fuzzy rule base.Then the multi-objective optimization model of shift point was set up with the objectives of regenerative braking energy recovery and the shift jerk.The simulation analysis was carried out under the New European Driving Cycle (NEDC).Compared with the strategy of none shift energy recovery,the simulation results showed that the regenerative braking energy recovery was increased by 6.14%,while the jerk met the German standard.

Key words: electric vehicles, regenerative braking, fuzzy control, shift point, multi-objective optimization

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