[1]李 萍,朱晓璐,焦晓红.基于智能优化规则的并联混合动力汽车能量管理策略[J].燕山大学学报,2019,43(6):546-553.[doi:10.3969/j.issn.1007-791X.2019.06.010]
 LI Ping,ZHU Xiaolu,JIAO Xiaohong.Parallel hybrid electric vehicle energy management strategy based on intelligent optimization rules[J].Journal of YanShan University,2019,43(6):546-553.[doi:10.3969/j.issn.1007-791X.2019.06.010]
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基于智能优化规则的并联混合动力汽车能量管理策略
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《燕山大学学报》[ISSN:1007-791X/CN:13-1219/N]

卷:
43
期数:
2019年第6期
页码:
546-553
栏目:
计算机与控制工程
出版日期:
2019-11-30

文章信息/Info

Title:
Parallel hybrid electric vehicle energy management strategy based on intelligent optimization rules
文章编号:
1007-791X(2019)06-0546-08
作者:
李 萍朱晓璐焦晓红*
燕山大学 电气工程学院,河北 秦皇岛 066004
Author(s):
LI Ping ZHU Xiaolu JIAO Xiaohong
School of Electric Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China
关键词:
混合动力汽车能量管理策略规则控制粒子群算法
Keywords:
hybrid electric vehicleenergy managementrule controlparticle swarm optimization
分类号:
TP18
DOI:
10.3969/j.issn.1007-791X.2019.06.010
文献标志码:
A
摘要:
针对并联型混合动力汽车,为了提高燃油经济性,在满足驾驶性能和车辆动力要求的前提下,提出了一种基于智能优化规则的能量管理策略。首先,考虑发动机最优工作区和电池的荷电状态,根据一定的工程经验,选取合适的发动机最优工作区转矩和电池荷电状态的阈值,设计了基于规则的能量管理控制策略。然后,考虑到规则控制中一些阈值参数不确定的问题,应用了一种智能优化算法——粒子群算法优化规则控制策略的阈值参数。最后,将所设计的控制策略在多种国际标准工况下进行仿真对比,结果表明,较纯发动机运行而言,普通规则控制策略可以平均节省14.9%的燃油,而基于智能优化规则的控制策略可以平均节省22%的燃油。
Abstract:
In order to improve fuel economy, an energy management strategy based on intelligent optimization rules is proposed under the premise of satisfying driving performance and vehicle power demands for parallel hybrid electric vehicles. Firstly, considering the optimal engine operating area and battery state of charge, the appropriate engine optimalworking area torque and batterys tate of charge threshold are selected based on certainhistorical experience to design a rule-based energy management control strategy. Then, considering the uncertainty of some parameter thresholds in the rule control strategy, an intelligent algorithm, particle swarm optimization, is used to optimize the threshold parameters of the rule-based control strategy. Finally, the proposed control strategy is simulated and compared under a variety of standard operating conditions. The results show that, compared with pure engine operation, the ordinary rule control strategy can save averagely 14.9% fuel, while the intelligent optimization rule-based control strategy can saveaveragely 22% fuel.

相似文献/References:

[1]景 远,焦晓红.基于交通信息和模型预测控制的混合动力汽车能量管理策略综述[J].燕山大学学报,2019,43(4):319.[doi:10.3969/j.issn.1007-791X.2019.04.006]
 JING Yuan,JIAO Xiaohong.Review on energy management strategies for hybrid electric vehicles based on traffic information and model predictive control[J].Journal of YanShan University,2019,43(6):319.[doi:10.3969/j.issn.1007-791X.2019.04.006]

备注/Memo

备注/Memo:
收稿日期:2018-06-12        责任编辑:孙峰
基金项目: 国家自然科学基金资助项目(61573304);河北省自然科学基金资助项目(F2017203210)
作者简介:李萍(1991-),女,山东临沂人,硕士研究生,主要研究方向为混合动力汽车能量管理;*通信作者:焦晓红(1966-),山西太原人,女,博士,教授,博士生导师,主要研究方向为非线性系统、时滞系统的鲁棒自适应控制及其在混合动力系统、机械系统和电力系统中的应用,Email:jiaoxh@ysu.edu.cn
更新日期/Last Update: 2020-01-03