Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application

被引:146
作者
Chen, Zeyu [1 ]
Xiong, Rui [2 ]
Lu, Jiahuan [1 ]
Li, Xinggang [2 ]
机构
[1] Northeastern Univ, Sch Mech Engn & Automat, Shenyang 110819, Liaoning, Peoples R China
[2] Beijing Inst Technol, Sch Mech Engn, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
All-climate electric vehicles; Battery safety; Abusing test; External short circuit; Temperature prediction; Fault detection; SUPPORT VECTOR MACHINE; STATE; CELLS; CHARGE; MODEL; POWER; ELECTROLYTE; MANAGEMENT; SAFETY;
D O I
10.1016/j.apenergy.2018.01.068
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
External short circuit (ESC) is a severe fault that can cause the large current and high temperature of lithium-ion batteries (LiBs) immediately. Temperature rise prediction is crucial for LiB safety management in an all-climate electric vehicles application because many disastrous consequences are caused by high temperature. This study mainly investigates the ESC-caused temperature rise characteristics of LiB, and proposes an online prediction approach of the maximum temperature rise. Three original contributions are made: (1) Abusing tests of LiBs under ESC are conducted at varying ambient temperatures, and the influences of battery state of charge (SOC) and ambient temperature on the maximum temperature rise are revealed. (2) Characteristics of temperature rises are analysed, therein finding that the heat generation of LiBs caused by ESC presents two modes: Joule heat dominant mode and reaction heat/Joule heat blended mode; leakage is an external manifestation of the latter. (3) Two heat generation modes are proved to be linearly separable at temperature rise discharge capacity plane, and then a two-step prediction approach of maximum temperature rise is proposed based on support vector machine. Finally, the presented approach is validated by the experimental data. The maximum temperature rise can be predicted up to 22.3 s ahead of time and very precise prediction results are obtained, where the mean prediction error for the eight test cells is 3.05%.
引用
收藏
页码:375 / 383
页数:9
相关论文
共 31 条
  • [1] Safety mechanisms in lithium-ion batteries
    Balakrishnan, PG
    Ramesh, R
    Kumar, TP
    [J]. JOURNAL OF POWER SOURCES, 2006, 155 (02) : 401 - 414
  • [2] Applying support vector machine to predict the critical heat flux in concentric-tube open thermosiphon
    Cai, Jiejin
    [J]. ANNALS OF NUCLEAR ENERGY, 2012, 43 : 114 - 122
  • [3] Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles
    Chen, Zeyu
    Xiong, Rui
    Tian, Jinpeng
    Shang, Xiong
    Lu, Jiahuan
    [J]. APPLIED ENERGY, 2016, 184 : 365 - 374
  • [4] Mechanical testing and macro-mechanical finite element simulation of the deformation, fracture, and short circuit initiation of cylindrical Lithium ion battery cells
    Greve, Lars
    Fehrenbach, Clemens
    [J]. JOURNAL OF POWER SOURCES, 2012, 214 : 377 - 385
  • [5] Krisher T., 2013, 3 FIRE TESLA MODELS
  • [6] State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge
    Lee, Seongjun
    Kim, Jonghoon
    Lee, Jaemoon
    Cho, B. H.
    [J]. JOURNAL OF POWER SOURCES, 2008, 185 (02) : 1367 - 1373
  • [7] Internal short circuit in Li-ion cells
    Maleki, Hossein
    Howard, Jason N.
    [J]. JOURNAL OF POWER SOURCES, 2009, 191 (02) : 568 - 574
  • [8] Improving wettability and preventing Li-ion batteries from thermal runaway using microchannels
    Mohammadian, Shahabeddin K.
    Zhang, Yuwen
    [J]. INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2018, 118 : 911 - 918
  • [9] Lithium iron phosphate based battery - Assessment of the aging parameters and development of cycle life model
    Omar, Noshin
    Monem, Mohamed Abdel
    Firouz, Yousef
    Salminen, Justin
    Smekens, Jelle
    Hegazy, Omar
    Gaulous, Hamid
    Mulder, Grietus
    Van den Bossche, Peter
    Coosemans, Thierry
    Van Mierlo, Joeri
    [J]. APPLIED ENERGY, 2014, 113 : 1575 - 1585
  • [10] Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine
    Pan, Yong
    Jiang, Juncheng
    Wang, Rui
    Cao, Hongyin
    Cui, Yi
    [J]. JOURNAL OF HAZARDOUS MATERIALS, 2009, 164 (2-3) : 1242 - 1249