Research of Multi-objective Parameter Decoupled Optimization Method for Hybrid Electric Vehicle

Yiran Zhang; Han Zhao; Kang Huang; Mingming Qiu

doi:10.16578/j.issn.1004.2539.2019.06.002

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Research of Multi-objective Parameter Decoupled Optimization Method for Hybrid Electric Vehicle

Theory·Reserach | 更新时间：2022-10-01

- Research of Multi-objective Parameter Decoupled Optimization Method for Hybrid Electric Vehicle
- Journal of Mechanical Transmission Vol. 43, Issue 6, Pages: 6-12(2019)
- 作者机构：
  
  1.合肥工业大学机械工程学院，安徽合肥 230009
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.06.002
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张怡然,赵韩,黄康等.混合动力汽车多目标参数解耦优化方法研究[J].机械传动,2019,43(06):6-12.

Zhang Yiran,Zhao Han,Huang Kang,et al.Research of Multi-objective Parameter Decoupled Optimization Method for Hybrid Electric Vehicle[J].Journal of Mechanical Transmission,2019,43(06):6-12.
张怡然,赵韩,黄康等.混合动力汽车多目标参数解耦优化方法研究[J].机械传动,2019,43(06):6-12. DOI： 10.16578/j.issn.1004.2539.2019.06.002.

Zhang Yiran,Zhao Han,Huang Kang,et al.Research of Multi-objective Parameter Decoupled Optimization Method for Hybrid Electric Vehicle[J].Journal of Mechanical Transmission,2019,43(06):6-12. DOI： 10.16578/j.issn.1004.2539.2019.06.002.

摘要

混合动力汽车的能量管理策略与动力总成参数高度耦合，在混合动力汽车优化过程中存在不断循环的情况，使得参数优化难以实现，无法找到最优解。针对这一问题，提出一种多参数解耦优化方法，该方法采用混合优化策略，将动力性目标作为约束条件，以粒子群算法优化动力总成参数，并通过粒子群算法对不同参数下的能量管理策略与换挡策略进行瞬时优化。针对一并联混合动力汽车，利用Matlab/Simulink建立了包含模糊PID驾驶员在内的用于优化过程中自适应的正向仿真模型。结果表明，混合优化算法能最大程度挖掘该动力总成的潜力，相较于同时优化逻辑门阈值的优化方法，经济性目标提高了4.55%，并能同时得到对应该最优参数的具有通用性的能量管理策略和换挡策略。

Abstract

The energy management strategy (EMS) and the powertrain parameter of hybrid electric vehicles are highly coupled. In the process of hybrid electric vehicle optimization, there is a continuous cycle, which makes it difficult to achieve parameter optimization and find the optimal solution. Aiming at this problem, a multi-parameter decouped optimization method is proposed, which adopts hybrid optimization strategy, taking the dynamic targets as constraint conditions and using particle swarm optimization algorithm to optimize powertrain parameter, the Particle swarm optimization (PSO) is used to optimize the energy management strategy and shifting strategy under different parameters. Aiming at a parallel hybrid vehicle, a forward model that includes a fuzzy PID driver is established by using Matlab/Simulink to self-adapt to the changing powertrain configurations. The results show that the hybrid optimization methodology is able to squeeze the potential of the vehicle, compared with the optimization method that simultaneously optimizes the logic gate threshold, the economy performance is enhanced by 4.55% and simultaneously, the energy management strategy and shifting strategy for the HEV under these parameters are obtained.

关键词

解耦优化逻辑门能量管理策略动力总成参数优化粒子群算法模糊PID驾驶员

Keywords

Decoupled optimizationLogic gate energy management strategyPowertrain parameter optimizationParticle swarm optimizationFuzzy PID driver

references

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