|本期目录/Table of Contents|

[1]周晓华,朱佳龙,冯雨辰.基于双启发式动态规划的PHEV能量管理策略[J].工业仪表与自动化装置,2023,(03):99-105+133.[doi:10.19950/j.cnki.cn61-1121/th.2023.03.020]
　Zhou Xiaohua,Zhu Jialong,Feng Yuchen.PHEV energy management strategy based on dual heuristic dynamic programming[J].Industrial Instrumentation & Automation,2023,(03):99-105+133.[doi:10.19950/j.cnki.cn61-1121/th.2023.03.020]
点击复制

基于双启发式动态规划的PHEV能量管理策略

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2023年03期
页码:
99-105+133
栏目:
出版日期:
2023-06-15

文章信息/Info

Title:
PHEV energy management strategy based on dual heuristic dynamic programming
文章编号:
1000-0682（2023）02-0099-07
作者:
周晓华1; 2; 朱佳龙1; 冯雨辰1
1.广西科技大学 自动化学院；
2.广西汽车零部件与整车技术重点实验室（广西科技大学），广西 柳州 545616
Author(s):
Zhou Xiaohua1; 2;  Zhu Jialong1;  Feng Yuchen1
1.School of Automation, Guangxi University of Science and Technology;?div>2.Guangxi Key Laboratory of Auto Parts and Vehicle Technology (Guangxi University of Science and Technology), Guangxi Liuzhou 545616,China
关键词:
插电式混合动力汽车; 双启发式动态规划; 执行依赖启发式动态规划; 燃油经济性
Keywords:
PHEV;  double heuristic dynamic programming;  action dependent heuristic dynamic programming;  fuel economy
分类号:
U469.79
DOI:
10.19950/j.cnki.cn61-1121/th.2023.03.020
文献标志码:
A
摘要:
为提高插电式混合动力汽车（PHEV）燃油经济性和排放性能，提出一种基于双启发式动态规划（DHP）的能量管理控制策略。采用3层BP神经网络分别设计了DHP能量管理控制器的执行网络、模型网络和评价网络。利用Matlab/Simulink与ADVISOR平台，在CYC_NDEC循环工况下对DHP能量管理控制策略进行联合仿真验证，并与执行依赖启发式动态规划（ADHDP）控制算法设计的能量管理控制策略进行对比分析。结果表明，CYC_NEDC循环工况经过1个、2个与3个循环周期后，DHP能量管理控制策略的蓄电池组SOC维持在0.63以上，燃油消耗量分别降低了6.9%、4.1%与2.7%，尾气有害物排放量均有一定程度的降低。该方法可有效提升整车的燃油经济性和环保性。
Abstract:
In order to improve the fuel economy and emission performance of plug-in hybrid electric vehicle (PHEV), an energy management control strategy based on double heuristic dynamic programming (DHP) was proposed. Three-layer BP neural network is used to design the execution network, model network and evaluation network of DHP energy management controller. Using MATLAB/Simulink and ADVISOR platform, the energy management control strategy of DHP is jointly simulated and verified under CYC_NDEC cycle condition, and compared with the energy management control strategy designed by the action dependent heuristic dynamic programming (ADHDP) control algorithm. The results show that the final SOC value of the battery pack using DHP energy management control strategy is maintained above 0.63, the fuel consumption is reduced by 6.9%, 4.1% and 2.7% respectively, and the exhaust harmful substances emissions are reduced to some extent, after one, two and three cycles under CYC_NEDC cycle, .Under the condition of ensuring good driving performance, this method can effectively improve the fuel economy and environmental protection of the whole vehicle.

参考文献/References:

[1]王洋洋,刘庆伟,罗哲.插电式混合动力汽车等效燃油消耗最小能量管理策略[J].汽车技术,2020,(5):8-21.

[2]Yu H F, Liu L, Bai B X, et al. A dynamic programming based control strategy with optimum efficiency of hybrid energy storage system for HEV[J]. Advanced Materials Research, 2015, 1092-1093:165-168.
[3]Larsson V, Johannesson L, Egardt B. Analytic solutions to the dynamic programming subproblem in hybrid vehicle energy management. IEEE Transactions on Vehicular Technology, 2015, 64(4):1458-1467.
[4]FU,Z C,CHEN Q H,ZHANG,LI Y,et al.Research on ADHDP energy management strategy for fuel cell hybrid power system[J].International journal of hydrogen energy，2021，46(57)：29432-29442.?div>[5] Wang Y, Jiao X. Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles[J]. Energies, 2022, 15(9): 3235.?/div>
[6]Kong Y, Xu N, Liu Q, et al. A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model, Energy (2023).
[7]谷贵志,赵咪,樊然然.基于模糊控制的PHEV转矩协调控制策略研究[J].农业装备与车辆工程,2021,59(3):103-107.
[8]夏长高,周雯雯,魏婕.并联式PHEV模糊控制策略研究[J].机械设计与制造,2019,(3):265-268,272.
[9]魏丽青,万幸.插电式混合动力汽车车速预测及能量管理策略[J].车用发动机,2022，(3):69-75.
[10]张冰战,李开放.基于动态规划的插电式混合动力汽车全局最优控制策略研究[J].汽车技术,2018,(7):16-21.
[11]陈渠,殷承良,张建龙,等.基于实时路况信息的插电式混合动力汽车预测性能量理算法研究[J].汽车技术,2020,(8):22-27.
[12]杨永军,李忠利,贾方,等.多模式混合动力汽车控制策略研究[J].车用发动机,2022，(1):64-70.
[13]李志鹏,赵杨.纯电动汽车电池管理系统及SOC精确估计[J].电源技术,2016,40(5):1090-1093.
[14]杜常清,甘雯雯,张佩.基于动态规划不同优化目标的HEV转矩分配策略对比研究[J].计算机应用研究,2017,34(5):1308-1310，1336.
[15]杨忠君,樊立萍,宗学军.基于DHP方法的PEM燃料电池优化控制器设计[J].电源技术,2014,38(11):2007-2009.
[16]袁君,章云,张桂东,等.基于自适应动态规划的能量管理系统研究综述[J].广东工业大学学报,2022,39(5):21-28.
[17]武小兰,白志峰,史小辉.PHEV模糊能量管理策略优化设计[J].系统仿真学报,2018,30(1):242-248.

相似文献/References:

备注/Memo

备注/Memo:
收稿日期：2023-01-07

基金项目：
广西自然科学基金重点项目（2020GXNSFDA238011）；
广西高校中青年教师科研基础能力提升项目（2022KY0331）

第一作者：
周晓华（1976—），男，云南牟定县人，硕士，副教授，研究方向为新能源汽车能量管理与控制、电机控制等。
更新日期/Last Update: 1900-01-01