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浙江大学学报(工学版)  2021, Vol. 55 Issue (8): 1538-1547    DOI: 10.3785/j.issn.1008-973X.2021.08.015
机械工程     
混合动力汽车经济型巡航的车速规划策略
鞠飞1(),庄伟超2,王良模1,*(),刘经兴1,王群1
1. 南京理工大学 机械工程学院,江苏 南京 210094
2. 东南大学 机械工程学院,江苏 南京 211189
Velocity planning strategy for economic cruise of hybrid electric vehicles
Fei JU1(),Wei-chao ZHUANG2,Liang-mo WANG1,*(),Jing-xing LIU1,Qun WANG1
1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2. School of Mechanical Engineering, Southeast University, Nanjing 211189, China
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摘要:

选取功率分流式混合动力汽车为对象,以燃油消耗最小为目标开展巡航场景下的经济车速规划研究. 结合车辆动能管理与等效燃油最小化策略(ECMS),提出增强型等效燃油最小化策略(R-ECMS). 运用极小值原理推导油电等效系数,建立动能与电能间的等效关系;结合电能与燃油之间的等效关系,将车辆动能变化和电能消耗统一转化成燃油消耗. 为了兼顾电池SOC平衡以及车辆通行速度,采取非支配排序遗传算法优化R-ECMS权重系数中的参数. 仿真结果表明,与传统能量管理策略ECMS相比,R-ECMS可以降低8.06%的燃油消耗. 与采用最优算法的动态规划策略相比,R-ECMS能在实现次优的优化效果的同时大幅降低计算时间. 同时,与ECMS相比,R-ECMS在其他仿真场景下能实现6.94%的节油率,具有较好的泛化性能和应用前景.

关键词: 汽车工程瞬时控制策略等效燃油消耗混合动力汽车动态规划遗传算法    
Abstract:

Economic velocity planning in cruise scenario was studied for the power-split hybrid electric vehicle aiming at minimizing the fuel consumption. A reinforced equivalent consumption minimization strategy (R-ECMS) was proposed based on kinetic energy management and equivalent consumption minimization strategy (ECMS). The equivalent coefficient between fuel and electric energy was derived using minimum principle. Meanwhile, the equivalent relationship between kinetic energy and electric energy was established. The vehicle kinetic change and electric consumption were unified into fuel consumption, combined with the equivalent relationship between fuel and electric energy. The non-dominated sorting genetic algorithm was adopted to optimize the parameters in the weight coefficients of the proposed strategy, to ensure battery SOC balance and meet vehicle travel time at the same time. Simulation results demonstrate that R-ECMS can reduce fuel consumption by 8.06% compared with the traditional control strategy ECMS. The proposed R-ECMS not only achieves sub-optimal optimization performance, but also sharply reduces the computing burden, as compared to dynamic programming. Moreover, its performance is robust to various driving scenarios. The simulation using another road profile in reality shows that the R-ECMS can achieve a 6.94% reduction in the corresponding fuel consumption compared to the ECMS. Thus, the R-ECMS has a good application prospect.

Key words: vehicle engineering    instantaneous control strategy    equivalent fuel consumption    hybrid electric vehicle    dynamic programming    genetic algorithm
收稿日期: 2020-08-26 出版日期: 2021-09-01
CLC:  U 461  
基金资助: 国家自然科学基金青年科学基金资助项目(51805081)
通讯作者: 王良模     E-mail: jufei@njust.edu.cn;liangmo@njust.edu.cn
作者简介: 鞠飞(1993—),男,博士生,从事新能源汽车节能驾驶研究. orcid.org/0000-0002-5485-4533. E-mail: jufei@njust.edu.cn
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引用本文:

鞠飞,庄伟超,王良模,刘经兴,王群. 混合动力汽车经济型巡航的车速规划策略[J]. 浙江大学学报(工学版), 2021, 55(8): 1538-1547.

Fei JU,Wei-chao ZHUANG,Liang-mo WANG,Jing-xing LIU,Qun WANG. Velocity planning strategy for economic cruise of hybrid electric vehicles. Journal of ZheJiang University (Engineering Science), 2021, 55(8): 1538-1547.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.08.015        https://www.zjujournals.com/eng/CN/Y2021/V55/I8/1538

图 1  功率分流式混合动力系统构型
图 2  行星齿轮排的功率分流原理
部位 参数 数值
车辆 整备质量m /kg 1450
空气阻力系数CD 0.28
迎风面积Af /m2 2.52
滚动阻力系数f 0.015
车轮半径Rtire /m 0.287
主减速比FR 3.3
发动机 最大功率/kW 73
最大扭矩/Nm 142
电机MG1 最大功率/kW 28
电机MG2 最大功率/kW 40
电池 容量/(kW·h) 4.4
行星齿轮 R1/S1R2/S2 3.30,2.63
表 1  功率分流式混合动力汽车的基本参数
图 3  动能权重系数与车速的关系
图 4  基于R-ECMS的混合动力汽车经济型巡航
图 5  海拔信息与速度规划仿真结果
图 6  燃油消耗和转矩信息
车速规划策略 ${\rm{F}}{{\rm{C}}_{{\rm{sum}}}}$/g $\Delta {\rm{SOC}}$ ${v_{{\rm{ave}}}}$/(km·h?1 ${t_{{\rm{run}}}}$/s
R-ECMS 64.52 ?0.011 8 59.00 8.5
ECMS 67.84 ?0.013 4 59.00 3.9
DP 62.27 ?0.004 7 58.39 32 980.0
表 2  R-ECMS、ECMS与DP的性能对比
图 7  真实道路海拔信息
权重特征值 初始值 优化范围 优化结果
${{{a}}_1}$ 8 [1,15] 9.345
${{{b}}_1}$ 0.7 [0.4,1.0] 0.911
${{{a}}_2}$ 8 [1,15] 13.241
${{{b}}_2}$ 0.7 [0.4,1.0] 0.731
表 3  权重特征值的初始值和优化范围
图 8  基于NSGA-II的R-ECMS优化架构
图 9  真实道路上的速度和SOC轨迹
图 10  真实道路上燃油消耗与转矩信息
车速规划策略 ${\rm{F}}{{\rm{C}}_{{\rm{sum}}}}$/g $\Delta {\rm{SOC}}$ ${v_{{\rm{ave}}}}$/(km·h?1) δ/%
DP 224.56 ?0.000 7 57.25 ?
R-ECMS(优化前) 284.26 0.025 5 61.34 6.52
ECMS 304.09 0.018 7 61.34 ?
R-ECMS(优化后) 244.46 0.001 7 60.47 8.06
ECMS 265.89 ?0.002 5 60.47 ?
表 4  不同巡航策略的性能对比
图 11  其他场景下R-ECMS的仿真结果
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