吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (8): 1741-1750.doi: 10.13229/j.cnki.jdxbgxb20210168

• 车辆工程·机械工程 • 上一篇    

增程式电动汽车增程器多点控制策略优化

刘汉武1(),雷雨龙1,阴晓峰2,付尧1(),李兴忠1   

  1. 1.吉林大学 汽车仿真与控制国家重点实验室,长春 130022
    2.西华大学 交通与汽车工程学院,成都 610039
  • 收稿日期:2021-03-05 出版日期:2022-08-01 发布日期:2022-08-12
  • 通讯作者: 付尧 E-mail:hwliu19@mails.jlu.edu.cn;fu_yao@jlu.edu.cn
  • 作者简介:刘汉武(1991-),男,博士研究生.研究方向:混合动力汽车理论与控制技术. E-mail:hwliu19@mails.jlu.edu.cn
  • 基金资助:
    四川省科技厅区域创新合作项目(2021YFQ0052);国家重点研发计划项目(2018YFB0104901);吉林省科技发展计划项目(20170204073GX)

Multi⁃point control strategy optimization for auxiliary power unit of range⁃extended electric vehicle

Han-wu LIU1(),Yu-long LEI1,Xiao-feng YIN2,Yao FU1(),Xing-zhong LI1   

  1. 1.State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China
    2.School of Transportation and Automotive Engineering,Xihua University,Chengdu 610039,China
  • Received:2021-03-05 Online:2022-08-01 Published:2022-08-12
  • Contact: Yao FU E-mail:hwliu19@mails.jlu.edu.cn;fu_yao@jlu.edu.cn

摘要:

针对增程式电动汽车增程器多点控制策略的优化问题,提出了一种基于多目标参数优化结果的增程器能量管理控制策略。首先,基于AVL-Cruise和Matlab-Simulink软件搭建整车系统仿真控制模型,基于NSGA-Ⅱ算法,以循环工况下的系统能耗、排放和电池容量衰减率为目标函数,以增程器最小持续工作时间为优化变量,构建了多目标优化模型。离线优化得到综合评估指标下的发动机最小持续工作时间Pareto最优解。设计了模糊控制器,在线实时调整发动机各工作点最小持续工作时间。结果表明:可实时调参的增程器多点控制策略能有效平衡能耗、排放和电池容量衰减率的关系,在有效降低能耗和排放的同时维持了较小的电池容量衰减率。

关键词: 车辆工程, 能量管理, 增程器控制策略, NSGA-Ⅱ算法, 多目标优化

Abstract:

Aiming at the multi-objective optimization(MOO) problem of multi-point control strategy for auxiliary power unit(APU) of the range-extended electric vehicle, an energy management control strategy for APU based on MOO results is proposed. Firstly, the vehicle simulation model was established on AVL-Cruise and Matlab-Simulink software, and a MOO model was built with the system energy consumption, emissions and battery capacity attenuation rate as the objective functions based on NSGA-Ⅱ algorithm, the minimum continuous working time of the engine was taken as the optimization variable. In off-line optimization, Pareto optimal solution was obtained under the comprehensive objective. An real-time adaptive fuzzy controller was designed and the minimum continuous working time of the engine was adjusted online. Simulation results show that the proposed strategy can effectively balance the relationship among energy consumption, emissions and battery capacity decay rate, while effectively reducing energy consumption and emissions while maintaining a small battery capacity loss rate.

Key words: vehicle engineering, energy management, auxiliary power unit control strategy, NSGA-Ⅱ algorithm, multi-objective optimization

中图分类号: 

  • U461.8

图1

多目标优化研究方法流程图"

图2

增程式电动汽车仿真模型"

表1

车辆模型参数"

参数数值参数数值
满载重量/kg1700旋转质量换算系数1.1
电池容量/kW?h20总传动比4.2
轴距/mm2865机械效率0.96
前轴到质心距离/mm1352低负荷功率/kW8
前轴到质心距离/mm1513中负荷功率/kW16
质心高度/mm500高负荷功率/kW28
迎风面积/m21.66电量维持阶段SoC0.3

图3

能量管理控制策略Simulink模型"

图4

基于NSGA-Ⅱ的参数多目标优化流程"

图5

模糊控制原理框图"

图6

模糊控制变量隶属度函数及输出变量MAP图"

图7

多目标优化下的Pareto解集"

图8

APU输出功率及电池电流"

图9

四种策略下的电池SoC的对比结果"

表2

评价函数Pareto解集范围"

观测指标μikminμikmax
Coil_ele0.7280.755
Ecom0.420.70
Qloss/%8.512.3
Icom0.650.83

表3

油-电转换效率仿真试验结果"

控制策略Coil_ele?Coil_ele/%
策略一0.739-
策略二0.735-14.82
策略三0.74833.33
策略四PACS0.731-29.63

表4

综合排放仿真试验结果"

控制策略Ecom?Ecom/%
策略一0.52-
策略二0.48-14.29
策略三0.5924.87
策略四PACS0.46-21.43

表5

电池容量衰减率仿真试验结果"

控制策略Qloss/10-3?Qloss/%
策略一11.2%-
策略二11.9%18.42
策略三8.8%-63.16
策略四PACS10.4%-21.11

表6

综合评价指数仿真试验结果"

控制策略Icom?Icom/%
策略一0.72-
策略二0.7623.53
策略三0.7411.76
策略四PACS0.8153.94

图10

台架结构示意图"

表7

试验台主要部件型"

部件型号
电涡流测功机CAMA CW-260
程控系统CAMA FST-4
功率分析仪YOKOGAWA WT-1800
永磁同步电机GLMP15L0

图11

台架试验结果"

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