A Multi-Objective Optimization Method for Energy Management Control of Hybrid Electric Vehicles Using NSGA-II Algorithm

doi:10.7652/xjtuxb201510023

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A Multi-Objective Optimization Method for Energy Management Control of Hybrid Electric Vehicles Using NSGA-II Algorithm

更新时间：2025-07-09

- A Multi-Objective Optimization Method for Energy Management Control of Hybrid Electric Vehicles Using NSGA-II Algorithm
- Vol. 49, Issue 10, Pages: 143-150(2015)
- 作者机构：
  
  重庆交通大学机电与车辆工程学院,重庆,400074
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201510023
  CLC： TP18;U469.7
- Online First：10 October 2015，
  
  Published：2015
- 稿件说明：
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A Multi-Objective Optimization Method for Energy Management Control of Hybrid Electric Vehicles Using NSGA-II Algorithm[J]. 2015, 49(10): 143-150.
DOI：

A Multi-Objective Optimization Method for Energy Management Control of Hybrid Electric Vehicles Using NSGA-II Algorithm[J]. 2015, 49(10): 143-150. DOI： 10.7652/xjtuxb201510023.

摘要

综合考虑燃油经济性、排放性与驾驶性对混合动力能量管理控制优化的优点

以某款并联混合动力汽车为研究对象

选取能量管理控制参数与传动系参数作为待优化参数

以动力性作为约束条件

建立混合动力能量管理控制多目标优化评价方法

提出基于NSGA-II算法的混合动力系统多目标优化方法

并与优化前控制策略进行仿真对比分析。结果表明:在满足基本约束的前提下

优化后燃油经济性最多提高了7.8%

平均提高了6.38%; 驾驶性性能指标最多提高了27.12%

平均提高了21.74%; 排放性综合指标平均提高了41.51%。提出的多目标优化算法具有良好的收敛性与分布性

得到的Pareto最优解集能够给混合动力能量管理控制策略提供更多的权衡选择方案

体现了多目标优化的优势。

Abstract

A multi-objective optimization evaluation method for hybrid electric vehicle(HEV)is proposed by comprehensively considering the influences of fuel economy

emission and drivability on the energy management control for HEV. The multi-objective optimization algorithm based on NSGA-II(non-dominated sorting genetic algorithm-II)is established by setting the parameters of the energy management control and the driveline system as the optimal parameters for the parallel hybrid electric vehicles

and the dynamic performance as the constraint condition. Then the proposed method is comparatively analyzed with the traditional control strategy that only considers the fuel economy. Simulation results show that the maximum fuel economy performance increases by 7.8% and the average value increases by 6.38%; the maximum drivability performance increases by 42.28% and the average value increases by 21.74%; the average synthetic emission performance increases by 41.51%. The proposed multi-objective optimization algorithm has good convergence and distribution. The obtained Pareto optimum solutions may provide more trade-off options for HEV energy management control strategy

which reflect the advantages of multi-objective optimization.

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references

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