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

doi:10.3969/j.issn.1674-8484.2022.04.001

Abstract

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

CLC Number:

U467.1+4

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.

Figures/Tables 12

References 197

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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