电动汽车混合储能系统自适应能量管理策略研究

doi:10.19799/j.cnki.2095-4239.2019.0232

摘要/Abstract

摘要：

为了增加电动汽车的续驶里程，提高混合储能系统的性能，本工作提出了一种混合储能系统自适应能量管理策略。首先，用模糊逻辑建立了驾驶员意图与驾驶员风格的识别模型，进而用该模型对低通滤波器的时间常数进行在线调整，用低通滤波器将电机需求功率分为平均功率和峰值功率。最后，在ADVISOR仿真平台上将所提出的自适应能量管理策略与传统的逻辑门限控制策略和模糊控制策略进行了对比，结果表明，自适应能量管理策略使得蓄电池放电电流峰值减小，能够使得电动汽车混合储能系统的功率分配更加合理，超级电容回收更多的制动能量，同时延长了续驶里程。

关键词: 电动汽车, 混合储能系统, 能量管理策略, 滤波器, 超级电容

Abstract:

This study proposes an adaptive energy-management strategy that enhances the driving range and performance of a hybrid energy-storage system. First, the intention and driving style of the driver is established by fuzzy logic. Using this model, the time constant of the low-pass filter is then adjusted online. The filter divides the motor demand power into the average power and the peak power. To evaluate its performance, the proposed energy management strategy is compared with the logic threshold control strategy and fuzzy control strategy on the ADVISOR simulation platform. The proposed adaptive energy-management strategy achieves lower peak discharge current of the battery than the other control strategies and improves the power distribution of hybrid energy-storage systems in electric vehicles. With these improvements, more braking energy is recovered by the ultracapacitor and the driving mileage is extended.

Key words: electric vehicle, hybrid energy storage system, energy management strategy, filter, ultracapacitor

中图分类号:

TP 391.9

张骞, 武小兰, 白志峰, 程靖宜. 电动汽车混合储能系统自适应能量管理策略研究[J]. 储能科学与技术, 2020, 9(3): 878-884.

ZHANG Qian, WU Xiaolan, BAI Zhifeng, CHENG Jingyi. Research on adaptive energy management strategy of hybrid energy storage system in electric vehicles[J]. Energy Storage Science and Technology, 2020, 9(3): 878-884.

图/表 12

图1

图2

表1

表2

表3

图3

表4

表5

图4

图5

图6

表6

参考文献 17

1	SULAIMAN N , HANNAN M A , MOHAMED A , et al . A review on energy management system for fuel cell hybrid electric vehicle: Issues and challenges[J]. Renewable and Sustainable Energy Reviews, 2015, 52: 802-814.
2	ENANG W , BANNISTER C . Modelling and control of hybrid electric vehicles (A comprehensive review)[J]. Renewable and Sustainable Energy Reviews, 2017, 74: 1210-1239.
3	罗玉涛, 刘秀田, 梁伟强, 等 . 延长锂离子电池寿命的电动汽车复合电源设计[J]. 华南理工大学学报(自然科学版), 2016, 44(3): 51-59.
	LUO Yutao , LIU Xiutian , LIANG Weiqiang , et al . Design of hybrid power system for prolonging lifespan of lithium-ion battery applied to electric vehicles[J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(3): 51-59.
4	王儒, 李训明, 魏伟, 等 . 基于ADVISOR的纯电动汽车复合电源系统[J]. 山东理工大学学报(自然科学版), 2014(1): 73-78.
	WANG Ru , LI Xunming , WEI Wei , et al . Hybrid power system of pure electric vehicle based on ADVISOR[J]. Journal of Shandong University of Technology (Natural Science Edition), 2014(1): 73-78.
5	ARAUJO R E , DE C R , PINTO C , et al . Combined sizing and energy management in EVs with batteries and supercapacitors[J]. IEEE Transactions on Vehicular Technology, 2014, 63(7): 3062-3076.
6	CASTAINGS A , LHOMME W , TRIGUI R , et al . Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints[J]. Appl. Energy, 2016, 163: 190-200.
7	ARANI S K , NIASAR A H , ZADEH A H . Energy management of dual-source propelled electric vehicle using fuzzy controller optimized via genetic algorithm[C]//2016 7th Power Electronics and Drive Systems Technologies Conference (PEDSTC). IEEE, 2016: 338-343.
8	CHEN J , XU C , WU C , et al . Adaptive fuzzy logic control of fuel-cell-battery hybrid systems for electric vehicles[J]. IEEE Transactions on Industrial Informatics, 2016, 14(1): 292-300.
9	LI L , YOU S X , YANG C , et al . Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses[J]. Applied Energy, 2016, 162: 868-879.
10	CHENG A Y , XIONG Q H , WANG L , et al . Design of drive control strategy for mini pure electric vehicles on the condition of slope climbing[C]//31th Chinese Control and Decision Conference(CCDC), 2019: 4828-4832.
11	LEI Y , LIU K , FU Y , et al . Research on driving style recognition method based on driver’s dynamic demand[J]. Advances in Mechanical Engineering, 2016, 8(9): 1-14.
12	DORR D , GRABENGIESSER D , GAUTERIN F . Online driving style recognition using fuzzy logic[C]//IEEE International Conference on Intelligent Transportation Systems, 2014: 1021-1026.
13	AHMED O A , BLEIJS J A M . An overview of DC-DC converter topologies for fuel cell-ultracapacitor hybrid distribution system[J]. Renewable and Sustainable Energy Reviews, 2015(42): 609-626.
14	张雷, 胡晓松, 王震坡 . 超级电容管理技术及在电动汽车中的应用综述[J]. 机械工程学报, 2017, 53(16): 32-43.
	ZHANG Lei , HU Xiaosong , WANG Zhenpo . Overview of supercapacitor management techniques in electrified vehicle applications[J]. Journal of Mechanical Engineering, 2017, 53(16): 32-43.
15	张亚桥, 程广伟, 周志立, 等 . 基于驾驶员意图识别的微型纯电动货车控制策略[J]. 河南科技大学学报(自然科学版), 2018, 39(5): 21-27.
	ZHANG Yaqiao , CHENG Guangwei , ZHOU Zhili , et al . Control strategy of micro electric truck based on driver intention recognition[J]. Journal of Henan University of Science & Technology (Natural Science), 2018, 39(5): 21-27.
16	高建平, 赵金宝, 杨松, 等 . 基于驾驶意图的插电式混合动力公交车控制策略[J]. 机械工程学报, 2016, 52(24): 107-114.
	GAO Jianping , ZHAO Jinbao , YANG Song , et al . Control strategy of plug-in hybrid electric bus based on driver intention[J]. Journal of Mechanical Engineering, 2016, 52(24): 107-114.
17	王庆年, 唐先智, 王鹏宇, 等 . 基于神经网络的混合动力汽车驾驶意图识别方法[J]. 农业机械学报, 2012, 43(8): 32-36.
	WANG Qingnian , TANG Xianzhi , WANG Pengyu , et al . Driving intention identification method for hybrid vehicles based on neural network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2012, 43(8): 32-36.

驾驶员的加速意图		ΔAcc
驾驶员的加速意图		L	M	H
Acc	L	B	B	M
	M	B	M	S
	H	M	S	S

τ			加速意图
τ			B	M	S
pow	T _r	B	S	MS	MS
		M	S	MS	M
		S	MS	M	M
eco	T _r	B	MS	M	M
		M	M	MB	B
		S	MB	B	B

超级电容放电深度的下限值		v<=20	20<v<=50	v>50
驾驶员模式	eco	0.35	0.2	0
驾驶员模式	pow	0.15	0.1	0

参数	数值
整车装备质量/kg	980
整车满载质量/kg	1220
轮胎半径/m	0.275
滚动阻力因数	0.018
迎风面积A/m²	2.17
空气阻力因数	0.3
电机峰值功率/kW	62
电机额定功率/kW	25.8
电容单体容量/F	200
电容单体质量/kg	0.15
电容单体电压/V（最大/最小）	3.8/1.0
电容数目/个	110
电池单体容量/A·h	80
电池单体质量/kg	5
电池单体电压/V（最大/最小）	16/9
电池数目/个	27

策略类型	续驶里程/km	单位工况的能量消耗/kW·h
自适应控制策略	149.0	1.6729
模糊控制策略	147.4	1.6813
逻辑门限控制策略	147.1	1.7082