新能源汽车节能规划与控制技术研究综述

doi:10.3969/j.issn.1674-8484.2022.04.001

汽车安全与节能学报 ›› 2022, Vol. 13 ›› Issue (4): 593-616.DOI: 10.3969/j.issn.1674-8484.2022.04.001

• 综述与展望 • 下一篇

新能源汽车节能规划与控制技术研究综述

孙超¹^,²(), 刘波¹, 孙逢春¹^,²^,^*()

1.北京理工大学电动车辆国家工程研究中心，北京 100081，中国
2.北京理工大学深圳汽车研究院，深圳 518118，中国

收稿日期:2022-11-25 修回日期:2022-08-24 出版日期:2022-12-31 发布日期:2023-01-01
通讯作者: 孙逢春
作者简介:*孙逢春(1958—)，男(汉)，湖南，教授，中国工程院院士。E-mail：sunfch@bit.edu.cn。
孙超(1988—)，男(汉)，河北，特别研究员/副教授。E-mail：chaosun@bit.edu.cn。特别研究员、副教授，博士，北京理工大学机械与车辆学院特别研究员、副教授、博士生导师; 北京理工大学深圳汽车研究院副院长，兼任深圳市未来智能网联交通系统产业创新中心副主任。主持国家自然科学基金重点项目、工信部产业技术基础公共服务平台项目和广东省重点领域研发计划项目等多项课题；以第一作者或通讯作者发表学术论文50余篇。主要从事智能车路协同、网联化汽车控制、非线性优化与机器学习理论和应用技术研究。
Prof. SUN Chao，He is a Ph.D., a research fellow/associate professor and doctoral supervisor of the School of Mechanical Engineering of Beijing Institute of Technology, the vice dean of Shenzhen Automotive Research Institute of Beijing Institute of Technology, and the deputy director of Shenzhen Future Intelligent Network Transportation System Industry Innovation Center. He has presided over many programs, such as the Key Program of the National Natural Science Foundation of China, the Industrial Technology Basic Public Service Platform Program of the Ministry of Industry and Information Technology, and the Key-Area Research and Development Program of Guangdong Province, and published more than 50 academic papers as the first/corresponding author. His research field covers cooperative vehicle infrastructure systems, connected vehicle control, nonlinear optimization and machine learning theory and application technology.
孙逢春教授，博士，北京理工大学机械与车辆学院教授、博士生导师；北京理工大学深圳汽车研究院院长，中国工程院院士。以第一完成人获国家技术发明奖二等奖2项、国家科技进步奖二等奖1项、国家级教学成果二等奖1项以及省部级奖励多项；出版著作8部，发表学术论文200余篇。主要从事电动车辆系统技术、电驱动系统关键技术、充换电站技术和清洁交通技术研究。
Prof. SUN Fengchun，He is a Ph.D., a professor and doctoral supervisor of the School of Mechanical Engineering of Beijing Institute of Technology, an academician of the Chinese Academy of Engineering, and the dean of Shenzhen Automotive Research Institute of Beijing Institute of Technology. He has won 2 second prizes in the State Technological Innovation Award, 1 second prize in the State Science and Technology Advancement Award, 1 second prize in the Teaching Achievement Award in Higher Education, and many provincial and ministerial awards as the first completer. He has published 8 books and more than 200 academic papers. His research field covers electric vehicle system technology, the key technology of electric drive systems, charging/swap station technology and clean transportation technology.
基金资助:
国家自然科学基金重点项目(U1964206)

Review of energy-saving planning and control technology for new energy vehicles

SUN Chao¹^,²(), LIU Bo¹, SUN Fengchun¹^,²^,^*()

1. National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
2. Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen 518118, China

Received:2022-11-25 Revised:2022-08-24 Online:2022-12-31 Published:2023-01-01
Contact: SUN Fengchun

摘要/Abstract

摘要：

通过车辆的运动规划与控制提升新能源汽车的节能效果，已经成为当前国内外聚焦的关键研究热点。该文总结了新能源汽车节能规划与控制技术的最新研究现状，分析了节能路径规划（eco-routing）、节能车速规划（eco-driving）、节能充电规划（eco-charging）、能量管理（energy management）和同时涉及以上多个领域的多任务优化技术。研究发现：虽然当前新能源汽车节能规划与控制技术已经取得了可观的研究进展，但在动态或随机交通行为场景下求解困难，综合考虑路径、速度和充电等深度关联行为的集成与协同优化仍需要探索，高价值的研究成果也有待从实验验证走向产业应用。今后新能源汽车节能规划与控制技术的未来发展趋势包括：1）考虑环境时变性和行为随机性的新问题；2）运用先进预测和高效求解等手段的新算法；3）系统解决多车多任务多维度问题的新方法；4）在真实场景下可复制推广的新应用。研究和解决以上问题对实现更高水平的新能源汽车节能控制具有重要意义。

关键词: 新能源汽车, 节能规划与控制, 路径规划, 车速规划, 充电规划, 能量管理

Abstract:

Improving the energy-saving effect of new energy vehicles through vehicle motion planning and control has become a key research focus at home and abroad. This paper summarizes the latest research status of energy-saving planning and control technology for new energy vehicles, and analyzes the eco-routing, eco-driving, eco-charging, energy management and multi-task optimization techniques involving multiple fields above. The study found that although the current energy-saving planning and control technology for new energy vehicles has made considerable research progress, it is difficult to solve the problem in dynamic or random traffic behavior scenarios, and the integrated and collaborative optimization, which considers deeply related behaviors such as path, speed and charging, remains to be explored, and the high-value research results also need to develop from experimental verification to industrial application. This paper proposes that the future development trends of energy-saving planning and control technology for new energy vehicles include: 1) new problems considering the time-varying environment and random behaviors; 2) new algorithms using advanced prediction and efficient solutions; 3) new methods to systematically solve multi-vehicle, multi-task and multi-dimensional problems; 4) new applications that can be replicated and promoted in real scenarios. studying and solving the above problems is of great significance to achieve a higher level of energy-saving control of new energy vehicles.

Key words: new energy vehicle, energy-saving planning and control, eco-routing, eco-driving, eco-charging, energy management

中图分类号:

U467.1+4

孙超, 刘波, 孙逢春. 新能源汽车节能规划与控制技术研究综述[J]. 汽车安全与节能学报, 2022, 13(4): 593-616.

SUN Chao, LIU Bo, SUN Fengchun. Review of energy-saving planning and control technology for new energy vehicles[J]. Journal of Automotive Safety and Energy, 2022, 13(4): 593-616.

图/表 12

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文献	车型	路段能耗/排放	约束			优化目标			求解方法
文献	车型	路段能耗/排放	时间	SOC/SOE	排放	能耗	排放	时间	求解方法
[18]	ICEV	指数模型				√			Dijkstra
[10]	ICEV	综合油耗				√			A*
[19]	BEV	NN+线性回归				√			Bellman-Ford
[14]	BEV	VT-CPEM				√		√	Integration内置
[12]	ICEV	多项式模型	√				(CO₂)		K则最短路径算法
[33]	ICEV	CMEM			(CO)			√	LP算法
[34]	BEV	综合电耗		√		√			IPM
[35]	BEV	综合电耗		√		√			PSO

文献	车型	路段能耗/排放	约束			优化目标			求解方法
文献	车型	路段能耗/排放	时间	SOC/SOE	排放	能耗	排放	时间	求解方法
[18]	ICEV	指数模型				√			Dijkstra
[10]	ICEV	综合油耗				√			A*
[19]	BEV	NN+线性回归				√			Bellman-Ford
[14]	BEV	VT-CPEM				√		√	Integration内置
[12]	ICEV	多项式模型	√				(CO₂)		K则最短路径算法
[33]	ICEV	CMEM			(CO)			√	LP算法
[34]	BEV	综合电耗		√		√			IPM
[35]	BEV	综合电耗		√		√			PSO

文献	车型	环境干扰					优化目标			时域T/ 空间域S	求解方法
文献	车型	动态坡度	动态限速	前车	信号灯	路口队列	能耗	时间	舒适性	时域T/ 空间域S	求解方法
[53]	ICEV	√					√			T	PMP
[67]	BEV			√			√			T	基于PMP的MPC
[56]	BEV		√	√	√		√			T	DP
[9]	ICEV				(随机)		√	√		S	DP
[55]	BEV				√		√	√	√	S	基于IDP的MPC
[70]	BEV	√	√	√			√		√	S	基于QCQP的MPC
[87]	ICEV				√		√	√	√	T	SQP
[88]	ICEV			√	√	√	√			T	混合求解方法
[59]	BEV			√	√		√			T	混合求解方法
[86]	ICEV			√			√			T	DDQN
[83]	ICEV			√	√			√		T	DDPG
[85]	BEV			√	√				√	T	TD3

文献	车型	环境干扰					优化目标			时域T/ 空间域S	求解方法
文献	车型	动态坡度	动态限速	前车	信号灯	路口队列	能耗	时间	舒适性	时域T/ 空间域S	求解方法
[53]	ICEV	√					√			T	PMP
[67]	BEV			√			√			T	基于PMP的MPC
[56]	BEV		√	√	√		√			T	DP
[9]	ICEV				(随机)		√	√		S	DP
[55]	BEV				√		√	√	√	S	基于IDP的MPC
[70]	BEV	√	√	√			√		√	S	基于QCQP的MPC
[87]	ICEV				√		√	√	√	T	SQP
[88]	ICEV			√	√	√	√			T	混合求解方法
[59]	BEV			√	√		√			T	混合求解方法
[86]	ICEV			√			√			T	DDQN
[83]	ICEV			√	√			√		T	DDPG
[85]	BEV			√	√				√	T	TD3

文献	车型	双向	分布式	优化目标			求解方法
文献	车型	双向	分布式	用户	聚合商	电网	求解方法
[107]	BEV	√				√	LP算法
[100]	BEV	√	√	√		√	ADMM
[101]	BEV	√		√			MILP算法
[108]	PHEV	√	√	√			逻辑控制
[104]	BEV	√		√		√	混合PSO
[95]	BEV			√			混合PSO
[105]	BEV	√	√	√			NN
[106]	BEV	√			√		DDPG

新能源汽车节能规划与控制技术研究综述

Review of energy-saving planning and control technology for new energy vehicles

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文献	车型	优化目标				优化方式	求解方法
文献	车型	行驶时间	充放电时间	充电成本	V2G收益	优化方式	路径规划	充电规划
[163]	BEV	√		√		耦合	自适应Dijkstra
[164]	BEV	√	√	√		耦合	A*
[165]	BEV			√	√	耦合	MILP求解器
[166]	BEV	√	√	√	√	耦合	MINLP求解器
[168]	BEV	√	√			耦合	混合元启发式
[169]	BEV			√	√	耦合	混合元启发式
[170]	BEV	√	√	√		解耦	LP算法	LP算法
[171]	BEV	√	√	√	√	解耦	LP算法	LP算法

文献	车型	能耗计算	优化方式	求解方法
文献	车型	能耗计算	优化方式	路径规划	能量管理
[175]	PHEV	模式优化	耦合	DP
[172]	PHEV	模式优化	耦合	MILP算法
[177]	P-HEV	功率分配	耦合	MILP算法
[176]	PS-PHEV	功率分配	耦合	RL
[173]	PHEV	模式优化	耦合	MILP算法
[173]	PHEV	模式优化	解耦	Dijkstra	LP算法
[178]	P-HEV	功率分配	解耦	二分搜索 + Bellman-Ford	半解析法(代理模型)
[179]	P-PHEV	功率分配	解耦	MILP算法	PMP(代理模型)

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