基于大数据的电动汽车用户行为对电池老化影响分析

doi:10.19562/j.chinasae.qcgc.2022.08.010

Abstract

Abstract:

In order to explore the influence of users' behaviors on the battery aging of electric vehicles， a statistical analysis method based on big data is proposed to study the influence of the region user's vehicle located， the charging mode preference and driving style of users on battery aging， relying on a large number of high-quality user and vehicle data on the enterprise supervision platform. The results show that with the increase of the average operating temperature of battery， the attenuation of battery capacity reduces first and then increases. The battery aging degree of Beijing users is 10.59%~19.09% higher than that of Guangdong users. With the rise of fast charging frequency， the attenuation rate of battery capacity gradually increases， but with the increasing rate declining. The battery aging of the user preferring fast charging is 33.45%~56.24% faster than that of users preferring slow charging. Aggressive driving mode may accelerate battery aging， with an aging rate 1.73%~10.37% faster than gentle driving mode. The research results have reference significance for vehicle enterprises to optimize vehicle function design and guide the users to drive healthily.

Key words: electric vehicles, battery aging, big data platform, user behavior

Haiqiang Liang,Hongwen He,Kangwei Dai,Bo Pang. Analysis on the Effects of User Behavior on Battery Aging of Electric Vehicles Based on Big Data[J].Automotive Engineering, 2022, 44(8): 1212-1217.

Figures/Tables 8

References 24

1	孟祥峰，孙逢春，林程，等. 动力电池循环寿命预测方法研究［J］. 电源技术， 2009， 33（11）：5.
	MENG X F， SUN F C， LIN C， et al. Cycle life prediction of power battery［J］. Chinese Journal of Power Sources， 2009， 33（11）：5.
2	BROUSSELY M， HERREYRE S， BIENSAN P， et al. Aging mechanism in Li-ion cells and calendar life predictions［J］. Journal of Power Sources， 2001，97–98（1）：13-21.
3	BARRÉ A， DEGUILHEM B， GROLLEAU S， et al. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications［J］. Journal of Power Sources， 2013，241（1）：680-689.
4	LI Y， TAKAHASHI M， WANG B F. A study on capacity fading of lithium-ion battery with manganese spinel positive electrode during cycling［J］. Electrochimica Acta，2006（51）：3228-3234.
5	刘伶，关昶.锂离子电池铝集流体腐蚀行为的研究进展［J］.电池工业，2011，16（2）：121-125.
	LIU L， GUAN C. Development progress of corrosion of aluminum used as current collector for Li-ion battery［J］. Chinese Battery Industry， 2011，16（2）：121-125.
6	WANG J， LIU P， HICKS-GARNER J， et al. Cycle-life model for graphite-LiFePO₄ cells［J］. Journal of Power Sources， 2011， 196（8）：3942-3948.
7	邹幽兰. 基于退役锂动力电池容量、内阻和荷电状态的建模与参数估计［D］. 长沙：中南大学， 2014.
	ZOU Y L. Modeling and parameter esrimation of retired lithium-ion power battery based on capacity、resistance and the state of charge［D］. Changsha： Central South University， 2014.
8	RAMADASS P， HARAN B， WHITE R， et al. Performance study of commercial LiCoO₂ and spinel-based Li-ion cells［J］. Journal of Power Sources，2002，111：210-220.
9	CHOI S S， HONG S L. Factors that affect cycle-life and possible degradation mechanisms of a Li-ion cell based on LiCoO₂［J］. Journal of Power Sources， 2002， 111（1）：130-136.
10	PETZL M， KASPER M， DANZER M A. Lithium plating in a commercial lithium-ion battery-a low-temperature aging study［J］. Journal of Power Sources， 2015， 275：799-807.
11	WRIGHT R B， MOTLOCH C G. Cycle life studies of advanced technology development program gen 1 lithium ion batteries［J］. Calendars， 2001.
12	ASAKURA K， SHIMOMURA M， SHODAI T. Study of life evaluation methods for Li-ion batteries for backup applications［J］. Journal of Power Sources， 2003， 119（Jun）：902-905.
13	RAMASAMY R P， WHITE R E， POPOV B N. Calendar life performance of pouch lithium-ion cells［J］. Journal of Power Sources， 2005， 141（2）：298-306.
14	孟祥怡，梁士福，张天强，等. 新能源汽车充电用户行为大数据分析及应用［J］. 汽车文摘， 2021（3）：6
	MENG X Y，LIANG S F， ZHANG T Q，et al. Big data analysis and application on user charging behaviors for new energy vehicles［J］. Automotive Digest， 2021（3）：6
15	班定东，黄祖朋，陈炼松，等. 新能源汽车大数据分析及其应用前景［J］.时代汽车，2020（14）：92-93.
	BAN D D， HUANG Z P， CHEN L S， et al. Big data analysis of new energy vehicles and its application prospect［J］. Auto Time，2020（14）：92-93.
16	王宁，舒雅静.数字化时代的汽车数据分析课程建设探究［J］.课程教育研究，2020（9）：231.
	WANG N， SHU Y J. Research on the course construction of automobile data analysis in the digital age［J］. Course Education Research， 2020（9）：231.
17	佘承其，张照生，刘鹏，等.大数据分析技术在电动汽车行业的应用综述——基于电动汽车运行大数据［J］.机械工程学报，2019，55（20）：3-16.
	SHE C Q， ZHANG Z S， LIU P， et al. Overview of the application of big data analysis technology in new energy vehicle industry：based on operating big data of new energy vehicle［J］. Journal of Mechanical Engineering， 2019，55（20）：3-16.
18	张文，王东，郑静楠，等.电动汽车领域的大数据研究与应用［J］.大众用电，2016（S2）：62-66.
	ZHANG W， WANG D， ZHENG J N，et al. Research and application of big data in the field of electric vehicles［J］. Popular Utilization of Electricity， 2016（S2）：62-66.
19	王思淼，张英杰，王芳，等.基于大数据的动力电池健康度估算方法研究［J］.传动技术，2021，35（4）：8-12，23.
	WANG S M， ZHANG Y J， WANG F， et al. Research on estimation method of power battery health based on big data［J］. Drive System Technique，2021，35（4）：8-12，23.
20	肖伟，钟卫东，舒小农，等.基于大数据的电池健康状态（SoH）的估算及应用［J］.汽车安全与节能学报，2019，10（1）：101-105.
	XIAO W， ZHONG W D， SHU X N，et al. Battery state of health （SoH） estimation method and application based on big data［J］. Journal of Automotive Safety and Energy，2019，10（1）：101-105.
21	张永志.车用锂离子动力电池健康状态识别与剩余寿命预测［D］.北京：北京理工大学，2018.
	ZHANG Y Z. State of health identification and remaining useful life prediction of lithium-ion batteries for electric vehicles［D］. Beijing ：Beijing Institute of Technology， 2018.
22	潘悦. 车用锂离子电池老化机理分析、建模与诊断研究［D］.北京：北京理工大学，2021.
	PAN Y. Research on aging mechanism analysis， modeling and diagnosis of lithium-ion batteries［D］. Beijing：Beijing Institute of Technology， 2021.
23	SHIM J， KOSTECKI R， RICHARDSON T， et al. Electrochemical and physical analysis of a Li-ion cell cycled at elevated temperature［J］. Journal of Power Sources， 2002， 112（1）.
24	李亚飞. 增程式电动汽车用锂离子动力电池寿命的估算与优化研究［D］.广州：华南理工大学，2015.
	LI Y F. Study on life estimation and optimization of lithium-ion power battery for extended range electric vehicle［D］. Guangzhou：South China University of Technology，2015.

[1]	WANG Dan, LIU Zhong-Chang, TIAN Jing, HAN Yong-Qiang, TAN Man-Zhi. [J]. , 2016, 38(12): 1415 -1419 .
[2]	LI Ling, MA Li, MOU Yu, XU Chao, LI Wen-Ru, SHI Shu-Ming. [J]. , 2016, 38(12): 1508 -1514 .
[3]	GAO Ji-Dong, GAO Bo-Lin, XIE Shu-Gang. [J]. , 2016, 38(12): 1515 -1520 .
[4]	QIN Xiao-Hui, WANG Jian-Qiang, XIE Bo-Yuan, HU Man-Jiang, LI Ke-Qiang. [J]. , 2017, 39(1): 73 -78 .
[5]	SU Chang, FU Li-Ming, WEI Jun, CAO Yue, HUANG Lei, LI Shuo. [J]. , 2017, 39(1): 35 -40 .
[6]	ZHANG Jian, ZHOU Dao-Kui, TONG Rui, QI Hao-Chen. [J]. , 2017, 39(1): 47 -51 .
[7]	ZHENG Wei. [J]. , 2017, 39(1): 107 -112 .
[8]	SHAO Shi-Ping, HUANG Ju-Hua, CAO Ming. [J]. , 2017, 39(2): 200 -205 .
[9]	Chen-Xin, FENG Xiao, SHEN Chuan-Liang, WANG Shuo, HU Cui-Song, LI Yan-Yang, YANG Chang-Hai-　. [J]. , 2017, 39(2): 206 -213 .
[10]	CAO Li-Bo, CHEN Zheng, YAN Ling-Bo, QIN Qin, ZHANG Rui-Feng. [J]. , 2017, 39(2): 225 -231 .

[1]	Zhipeng Jiao, Jian Ma, Xuan Zhao, Kai Zhang, Dean Meng, Qi Han, Zhao Zhang. Research on Short-Time Test Cycle and Method Based on Electric Vehicle Braking Safety Detection [J]. Automotive Engineering, 2024, 46(1): 109-119.
[2]	Jiangxin Yuan, Liping He, Yaodong Li, Gang Li. Thermal Analysis and Optimization Design of a BMS Slave Unit for Electric Vehicles [J]. Automotive Engineering, 2024, 46(1): 128-138.
[3]	Haiqiang Liang,Hongwen He,Kangwei Dai,Bo Pang,Peng Wang. Research on Lithium Ion Battery Life Prediction Method Based on Empirical Aging Model and Mechanism Model for Electric Vehicles [J]. Automotive Engineering, 2023, 45(5): 825-835.
[4]	Yapeng Li,Xiaolin Tang,Xiaosong Hu. Study on Eco-driving of PHEVS Based on Hierarchical Control Strategy [J]. Automotive Engineering, 2023, 45(4): 551-560.
[5]	Zhiheng Wu,Aimin Liu. Switching Functional Hybrid Control Strategy for Permanent Magnet Synchronous Motor of Electric Vehicle [J]. Automotive Engineering, 2023, 45(4): 619-627.
[6]	Cheng Zhu,Di Liu,Xinyu Teng,Guohua Zhang,Dan Yu,Sha Liu,Ningdan Hu. Comparative Analysis and Forecast Research on Comprehensive Economy of New Energy Vehicles [J]. Automotive Engineering, 2023, 45(2): 333-340.
[7]	Qin Li,Jianming Tang,Boyuan Zhang,Yong Chen,Yong Wang. Research on Fault-Tolerant Control of Multi-Actuator for Distributed Drive Electric Vehicles [J]. Automotive Engineering, 2023, 45(12): 2251-2259.
[8]	Jichao Hong,Fengwei Liang,Haixu Yang,Kerui Li. Research on Intelligent Safety Management and Control Methods for Big-data-driven Battery Systems [J]. Automotive Engineering, 2023, 45(10): 1845-1861.
[9]	Junnian Wang,Chunlin Zhang,Mengyuan Zhao,Yue Qiang,Dachang Guo,Fang Yang. Coordinated Shift Control of Hub-Motor Two-Speed Transmission Without Power Interruption [J]. Automotive Engineering, 2023, 45(10): 1885-1896.
[10]	Jianbin Wang,Yuanqing Liang,Shuang Wang. Intelligent Control Strategy of Fresh Air Ratio in Automobile Cabin Under Air-Conditioning Condition and Energy-Saving Effect Evaluation [J]. Automotive Engineering, 2023, 45(1): 147-156.
[11]	Cheng Lin,Sheng Liang,Xinle Gong,Xiao Yu,Bowen Wang. Integrated Dynamic Control Strategy for Extreme Maneuvers of 4WIDEVs [J]. Automotive Engineering, 2022, 44(9): 1372-1385.
[12]	Shaobo Xie,Pengcheng Qu,Jiacheng Li,Huiqing Wang,Kun Lang. Study on Coordinated Control of Speed Planning and Energy Management for Connected Hybrid Electric Truck in Vehicle Following Scene [J]. Automotive Engineering, 2022, 44(8): 1136-1143.
[13]	Yong Chen,Changyin Wei,Xiaoyu Li,Yanlin Li,Caixia Liu,Xiaozhe Lin. Research on Fuzzy Energy Management Strategy for Extended-Range Electric Vehicles with Driving Condition Identification [J]. Automotive Engineering, 2022, 44(4): 514-524.
[14]	Yu Chen,Xiaogang Wu,Jiuyu Du,Jinlei Sun. Research on Energy Management of Microgrid with Consideration of Taxi Charging Behavior [J]. Automotive Engineering, 2022, 44(4): 525-534.
[15]	Guanfeng Wang,Qiang Song,Lukai Zhao. Research on Compensation Algorithm for Load Simulation Accuracy of Electric Drive System Test Bench [J]. Automotive Engineering, 2022, 44(3): 426-433.

Analysis on the Effects of User Behavior on Battery Aging of Electric Vehicles Based on Big Data

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 24

Related Articles 15

Metrics

Comments

Recommended 10