Optimal Energy Management for Dual Motor Planetary Coupled PHEV Based on Multi-Objective Compensation Factor

doi:10.7652/xjtuxb201903018

您当前的位置：

首页 >

文章列表页 >

Optimal Energy Management for Dual Motor Planetary Coupled PHEV Based on Multi-Objective Compensation Factor

更新时间：2025-07-09

- Optimal Energy Management for Dual Motor Planetary Coupled PHEV Based on Multi-Objective Compensation Factor
- Vol. 53, Issue 3, Pages: 125-134(2019)
- 作者机构：
  
  1. 江苏大学汽车与交通工程学院,江苏,镇江,212013
  2. 江苏大学汽车工程研究院,江苏,镇江,212013
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201903018
  CLC： U462.3
- Online First：10 March 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
Optimal Energy Management for Dual Motor Planetary Coupled PHEV Based on Multi-Objective Compensation Factor[J]. 2019, 53(3): 125-134.
DOI：

Optimal Energy Management for Dual Motor Planetary Coupled PHEV Based on Multi-Objective Compensation Factor[J]. 2019, 53(3): 125-134. DOI： 10.7652/xjtuxb201903018.

摘要

针对某新型双电机行星耦合插电式混合动力汽车(PHEV)

以燃油经济性为研究目标

为改善以等效因子为核心的等效燃油瞬时消耗最小策略(ECMS)的控制效果

结合多动力源之间在行星齿轮机构中的耦合机理

建立油电等效因子自适应瞬态ECMS算法(A-ECMS)

在此基础上进一步引入车辆初始荷电状态(SOC)和行驶里程对油电等效因子的影响

根据不同驾驶条件对等效因子进行离线遗传优化

建立基于等效因子优化Map图的遗传优化ECMS能量管理策略(GA-ECMS)。进行了仿真与硬件在环试验

仿真结果表明:相比于传统ECMS以及A-ECMS

本文提出的GA-ECMS算法下车辆百公里燃油消耗量分别降低了6.5%和3.4%; 硬件在环试验结果与仿真结果趋势一致

表明了所制定的能量管理策略的有效性和可行性

从而可为建立不同初始SOC、不同行驶里程下PHEV功率分配策略提供理论基础。

Abstract

To improve the control effect of the instantaneous equivalent fuel consumption minimization strategy(ECMS)

taking fuel economy of a new type dual-motor planetary coupled plug-in hybrid electric vehicle(PHEV)as the research target

and based on the coupling mechanism between multiple power sources in planetary gear mechanism

an adaptive ECMS algorithm for the equivalent factors is presented. On this basis

the influences of vehicle initial state of charge(SOC)and driving mileage on the fuel-electricity equivalent factor is further studied

and the off-line genetic optimization of the equivalent factor is performed to obtain the Map of the compensation value for fuel-electricity equivalent factor. The genetically optimized adaptive ECMS energy management strategy based on the Map of the optimal equivalent factor is proposed. Hardware-in-loop simulation experiments are conducted and the results show that compared with the traditional ECMS and A-ECMS

the proposed GA-ECMS algorithm reduces fuel consumption by 6.5% and 3.4%

respectively. The results of HiL experiment and simulation are basically consistent

which verifies the feasibility and effectiveness of the proposed energy management strategy. It also provides a theoretical basis for making power allocation strategy for different vehicle initial SOCs and driving mileage.

关键词

Keywords

references

WANG W, SONG R, GUO M, et al. Analysis on compound-split configuration of power-split hybrid electric vehicle [J]. Mechanism & Machine Theory, 2014, 78(8): 272-288.

FINESSO R, SPESSA E, VENDITTI M. Cost-optimized design of a dual-mode diesel parallel hybrid electric vehicle for several driving missions and market scenarios [J]. Applied Energy, 2016, 177: 366-383.

NGO H T, YAN H S. Configuration synthesis of parallel hybrid transmissions [J]. Mechanism Machine Theory, 2016, 97(5): 51-71.

左义和, 项昌乐, 闫清东. 混联混合动力汽车动态规划算法研究 [C]∥2009中国汽车工程学会年会论文集. 北京: 中国汽车工程学会, 2009: 97-101.

羌嘉曦, 敖国强, 何建辉, 等. 电动汽车动力电池特性仿真系统 [J]. 上海交通大学学报, 2009, 43(8): 1196-1200.

QIANG Jiaxi, AO Guoqiang, HE Jianhui, et al. Simulation system of electric vehicle battery characteristics [J]. Journal of Shanghai Jiaotong University, 2009, 43(8): 1196-1200.

AHMADI S, BATHAEE S M T. Multi-objective genetic optimization of the fuel cell hybrid vehicle supervisory system: fuzzy logic and operating mode control strategies [J]. International Journal of Hydrogen Energy, 2015, 40(36): 12512-12521.

PROKHOROV D V. Toyota Prius HEV neurocontrol and diagnostics [J]. Neural Networks, 2008, 21(2/3): 458-465.

CHENG Y, TRIGUI R, ESPANET C, et al. Specifications and design of a PM electric variable transmission for Toyota Prius: II [J]. IEEE Transactions on Vehicular Technology, 2011, 60(9): 4106-4114.

ZHANG Q, DENG W, WU J. A rule based energy management system of experimental battery/supercapacitor hybrid energy storage system for electric vehicles[J/OL]. Journal of Control Science and Engineering, 2016. [2018-09-15]. DOI: 10.1155/2016/6828 269.

PENG J, HE H, XIONG R. Rule based energy management strategy for a series: parallel plug-in hybrid electric bus optimized by dynamic programming[J/OL]. Applied Energy, 2016. [2018-09-15]. DOI: 10.1016/j. apenergy. 2015.12.031.

FINESSO R, SPESSA E, VENDITTI M. Robust equivalent consumption-based controllers for a dual-mode diesel parallel HEV [J]. Energy Conversion Management, 2016, 127: 124-139.

SUN C, HE H, SUN F. The role of velocity forecasting in adaptive-ECMS for hybrid electric vehicles [J]. Energy Procedia, 2015, 75: 1907-1912.

HAN J, PARK Y, PARK Y S. A novel updating method of equivalent factor in ECMS for prolonging the lifetime of battery in fuel cell hybrid electric vehicle [J]. IFAC Proceedings Volumes, 2012, 45(30): 227-232.

SERRAO L, ONORI S, RIZZONI G. ECMS as a realization of Pontryagin's minimum principle for HEV control [C]∥American Control Conference. Piscataway, NJ, USA: IEEE, 2009: 3964-3969.

CHEN Z, MI C C, XIA B, et al. Energy management of power-split plug-in hybrid electric vehicles based on simulated annealing and Pontryagin's minimum principle [J]. Journal of Power Sources, 2014, 272: 160-168.

POURABDOLLAH M, MURGOVSKI N, GRAUERS A, et al. An iterative dynamic programming/convex optimization procedure for optimal sizing and energy management of PHEVs [J]. IFAC Proceedings Volumes, 2014, 47(3): 6606-6611.

PIUS P, RIZZONI G. A comparative study of supervisory control strategies for hybrid electric vehicles [J]. IEEE Transactions on Control Systems Technology, 2007, 15(3): 506-518.

CHEN Z, MI C C, XIONG R, et al. Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming [J]. Journal of Power Sources, 2014, 248(7): 416-426.

赵福水, 陶栋材, 龙英, 等. 混合动力汽车动力耦合系统结构与发展分析 [J]. 湖南农机, 2007(9): 12-13.

ZHAO Fushui, TAO Dongcai, LONG Ying, et al. Structure and development analysis of hybrid power vehicle dynamic coupling system [J]. Hunan Agricultural Machinery, 2007(9): 12-13.

朱福堂, 陈俐, 殷承良, 等. 混合动力汽车行星齿轮机构的方案设计与优选 [J]. 中国机械工程, 2010, 21(1): 104-109.

ZHU Futang, CHEN Li, YIN Chengliang, et al. Conceptual design and optimization of planetary gear mechanism for hybrid electric vehicle [J]. China Mechanical Engineering, 2010, 21(1): 104-109.

巴特, 高印寒, 王庆年, 等. 基于动态规划算法的并联混合动力客车控制策略优化 [J]. 汽车工程, 2015, 37(12): 1359-1365.

BA Te, GAO Yinhan, WANG Qingnian, et al. Optimization of control strategy for parallel hybrid electric bus based on dynamic programming algorithm [J]. Automobile Engineering, 2015, 37(12): 1359-1365.

邓涛, 罗俊林, 韩海硕, 等. 混合动力汽车工况识别自适应能量管理策略 [J]. 西安交通大学学报, 2018, 52(1): 77-83.

DENG Tao, LUO Junlin, HAN Haishuo, et al. Adaptive energy management strategy based on driving cycle identification for hybrid electric vehicle [J]. Journal of Xi'an Jiaotong University, 2018, 52(1): 77-83.

周楠, 王庆年, 曾小华. 基于工况识别的HEV自适应能量管理算法 [J]. 湖南大学学报(自科版), 2009, 36(9): 37-41.

ZHOU Nan, WANG Qingnian, ZENG Xiaohua. Adaptive energy management algorithm for condition recognition of hybrid electric vehicle [J]. Journal of Hunan University(Natural Sciences), 2009, 36(9): 37-41.

孙家文, 刘胜永, 王建超, 等. 基于转矩分配的混合动力汽车控制策略的优化研究 [J]. 广西科技大学学报, 2017, 28(1): 29-34.

SUN Jiawen, LIU Shengyong, WANG Jianchao, et al. Optimization of control strategy for hybrid electric vehicle based on torque distribution [J]. Journal of Guangxi University of Science and Technology, 2017, 28(1): 29-34.

秦大同, 曾育平, 苏岭, 等. 基于近似极小值原理的插电式混合动力汽车实时控制策略 [J]. 汽车工程, 2017, 39(9): 1004-1010.

QIN Datong, ZENG Yuping, SU Ling, et al. Real time control strategy for plug-in hybrid electric vehicle based on approximate minimum principle [J]. Automobile Engineering, 2017, 39(9): 1004-1010.

赵秀春, 郭戈. 混合动力电动汽车能量管理策略研究综述 [J]. 自动化学报, 2016, 42(3): 321-334.

ZHAO Xiuchun, GUO Ge. Review of energy management strategies for hybrid electric vehicles [J]. Journal of Automation, 2016, 42(3): 321-334.

吉根林. 遗传算法研究综述 [J]. 计算机应用与软件, 2004, 21(2): 69-73.

JI Genlin. Overview of genetic algorithm research [J]. Computer Applications and Software, 2004, 21(2): 69-73.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Optimized Epithermal Neutron Field Design Based on D-D Neutron Source

Optimization Design of Epithermal Neutron Field Based on D-D Neutron Source

Reinforcement Learning-Based Energy Management Strategy Considering Driving Style for Hybrid Electric Vehicle

Estimation of Failure Intensity and Maintenance Effect for Petrochemical Equipment

Component Placement Process Optimization for Dual-Gantry Placement Machine Based on Hybrid Genetic Algorithm

Related Author

JIANG Quanxu

HAN Zheng

LIN Xingyang

YANG Shanghui

DAI Yuqi

ZHANG Tianjiao

CHEN Tian

MA Baolong

Related Institution

华硼中子科技（杭州）有限公司，310000，杭州Hua Boron Neutron Technology（Hangzhou）Co., Ltd., Hangzhou 310000, China

西安交通大学湖州中子科学实验室，313000，浙江湖州Huzhou Neutron Science Laboratory, Xi'an Jiaotong University, Huzhou, Zhejiang 313000, China

西安交通大学核科学与技术学院，710049，西安School of Nuclear Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China

HBNCT Co., Ltd.,Shangcheng District

School of Nuclear Science and Technology, Xi’an Jiaotong University

⁰