A Critical Review of Thermal Runaway Prediction and Early-Warning Methods for Lithium-Ion Batteries
), Yizhao Gao()Abstract
Lithium-ion batteries are widely used in electric vehicles because of their high energy density and long cycle life. However, the spontaneous combustion accident of electric vehicles caused by thermal runaway of lithium-ion batteries seriously threatens passengers' personal and property safety. This paper expounds on the internal mechanism of lithium-ion battery thermal runaway through many previous studies and summarizes the proposed lithium-ion battery thermal runaway prediction and early warning methods. These methods can be classified into battery electrochemistry-based, battery big data analysis, and artificial intelligence methods. In this paper, various lithium-ion thermal runaway prediction and early warning methods are analyzed in detail, including the advantages and disadvantages of each method, and the challenges and future development directions of the intelligent lithium-ion battery thermal runaway prediction and early warning methods are discussed.
References
Dennehy ER, Gallachóir BPÓ. Ex-post decomposition analysis of passenger car energy demand and associated CO2 emissions. Transp Res Part D: Transp Environ. 2018;59:400–416.
Meyer BK, Klar PJ. Sustainability and renewable energies - a critical look at photovoltaics. Phys Status Solidi Rapid Res Lett. 2011;5(9):318–323.
Diouf B, Pode R. Potential of lithium-ion batteries in renewable energy. Renew Energy. 2015;76:375–380.
Blomgren GE. The development and future of lithium ion batteries. J Electrochem Soc. 2016;164(1):A5019–A5025.
Noh HJ, Youn S, Yoon CS, Sun YK. Comparison of the structural and electrochemical properties of layered [LiNixCoyMnz]O2 (x = 1/3, 0.5, 0.6, 0.7, 0.8 and 0.85) cathode material for lithium-ion batteries. J Power Sources. 2013;233:121–130.
Wang Q, Mao B, Stoliarov SI, Sun J. A review of lithium ion battery failure mechanisms and fire prevention strategies. Prog Energy Combust Sci. 2019;73:95–131.
Ribière P, Grugeon S, Morcrette M, Boyanov S, Laruelle S, Marlair G. Investigation on the fire-induced hazards of Li-ion battery cells by fire calorimetry. Energy Environ Sci. 2012;5(1):5271–5280.
Feng X, Ouyang M, Liu X, Lu L, Xia Y, He X. Thermal runaway mechanism of lithium ion battery for electric vehicles: A review. Energy Storage Mater. 2018;10:246–267.
Wang Q, Ping P, Zhao X, Chu G, Sun J, Chen C. Thermal runaway caused fire and explosion of lithium ion battery. J Power Sources. 2012;208:210–224.
Cai T, Stefanopoulou AG, Siegel JB. Early detection for Li-ion batteries thermal runaway based on gas sensing. ECS Trans. 2019;89(1–2):85–97.
Parekh MH, Li B, Palanisamy M, Adams TE, Tomar V, Pol VG. In situ thermal runaway detection in lithium-ion batteries with an integrated internal sensor. ACS Appl Energy Mater. 2020;3(8):7997–8008.
Esho I, Shah K, Jain A. Measurements and modeling to determine the critical temperature for preventing thermal runaway in Li-ion cells. Appl Therm Eng. 2018;145:287–294.
Wang M, Lei S, Pengyu G, Dongliang G, Lantian Z, Yang J. Overcharge and thermal runaway characteristics of lithium iron phosphate energy storage battery modules based on gas online monitoring. High Volt Eng. 2021;47(1):279–286.
Cai T, Stefanopoulou AG, Siegel JB. Modeling Li-ion battery temperature and expansion force during the early stages of thermal runaway triggered by internal shorts. J Electrochem Soc. 2019;166(12):A2431–A2443.
Tran MK, Fowler M. A review of lithium-ion battery fault diagnostic algorithms: Current progress and future challenges. Algorithms. 2020;13(3):62.
Lee CC. Fuzzy logic in control systems: Fuzzy logic controller. I. IEEE Trans Syst Man Cybern. 1990;20(2):404–418.
Feng X, Zheng S, Ren D, He X, Wang L, Cui H, Liu X, Jin C, Zhang F, Xu C, et al. Investigating the thermal runaway mechanisms of lithium-ion batteries based on thermal analysis database. Appl Energy. 2019;246:53–64.
Wen J, Yu Y, Chen C. A review on lithium-ion batteries safety issues: Existing problems and possible solutions. Mater Express. 2012;2(3):197–212.
Shurtz RC, Hewson JC. Review-materials science predictions of thermal runaway in layered metal-oxide cathodes: A review of thermodynamics. J Electrochem Soc. 2020;167(9):Article 090543.
Tsukasaki H, Fukuda W, Morimoto H, Arai T, Mori S, Hayashi A, Tatsumisago M. Thermal behavior and microstructures of cathodes for liquid electrolyte-based lithium batteries. Sci Rep. 2018;8(1):15613.
Gachot G, Grugeon S, Eshetu GG, Mathiron D, Ribière P, Armand M, Laruelle S. Thermal behaviour of the lithiated-graphite/electrolyte interface through GC/MS analysis. Electrochim Acta. 2012;83:402–409.
Shah K, Chalise D, Jain A. Experimental and theoretical analysis of a method to predict thermal runaway in Li-ion cells. J Power Sources. 2016;330:167–174.
Shah K, Jain A. Prediction of thermal runaway and thermal management requirements in cylindrical Li-ion cells in realistic scenarios. Int J Energy Res. 2019;43(5):1827–1838.
Kim GH, Pesaran A, Spotnitz R. A three-dimensional thermal abuse model for lithium-ion cells. J Power Sources. 2007;170(2):476–489.
Ping P, Wang Q, Chung Y, Wen J. Modelling electro-thermal response of lithium-ion batteries from normal to abuse conditions. Appl Energy. 2017;205:1327–1344.
Feng X, He X, Ouyang M, Wang L, Lu L, Ren D, Santhanagopalan S. A coupled electrochemical-thermal failure model for predicting the thermal runaway behavior of lithium-ion batteries. J Electrochem Soc. 2018;165(16):A3748–A3765.
Wang Y, Ren D, Feng X, Wang L, Ouyang M. Thermal runaway modeling of large format high-nickel/silicon-graphite lithium-ion batteries based on reaction sequence and kinetics. Appl Energy. 2022;306:Article 117943.
Fu R, Choe S-Y, Agubra V, Fergus J. Modeling of degradation effects considering side reactions for a pouch type Li-ion polymer battery with carbon anode. J Power Sources. 2014;261:120–135.
Abada S, Petit M, Lecocq A, Marlair G, Sauvant-Moynot V, Huet F. Combined experimental and modeling approaches of the thermal runaway of fresh and aged lithium-ion batteries. J Power Sources. 2018;399:264–273.
Zhang L, Zhao P, Xu M, Wang X. Computational identification of the safety regime of Li-ion battery thermal runaway. Appl Energy. 2020;261:Article 114440.
Zhang Y, Mei W, Qin P, Duan Q, Wang Q. Numerical modeling on thermal runaway triggered by local overheating for lithium iron phosphate battery. Appl Therm Eng. 2021;192:Article 116928.
Ren D, Feng X, Lu L, Ouyang M, Zheng S, Li J, He X. An electrochemical-thermal coupled overcharge-to-thermal-runaway model for lithium ion battery. J Power Sources. 2017;364:328–340.
An Z, Shah K, Jia L, Ma Y. Modeling and analysis of thermal runaway in Li-ion cell. Appl Therm Eng. 2019;160:Article 113960.
Ren D, Liu X, Feng X, Lu L, Ouyang M, Li J, He X. Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components. Appl Energy. 2018;228:633–644.
Doyle M, Newman J. Analysis of capacity-rate data for lithium batteries using simplied models of the discharge process. J Appl Electrochem. 1997;27(7):846–856.
Golubkov AW, Fuchs D, Wagner J, Wiltsche H, Stangl C, Fauler G, Voitic G, Thaler A, Hacker V. Thermal-runaway experiments on consumer Li-ion batteries with metal-oxide and olivin-type cathodes. RSC Adv. 2014;4(7):3633–3642.
Lammer M, Königseder A, Hacker V. Holistic methodology for characterisation of the thermally induced failure of commercially available 18650 lithium ion cells. RSC Adv. 2017;7(39):24425–24429.
Yang H, Bang H, Amine K, Prakash J. Investigations of the exothermic reactions of natural graphite anode for Li-ion batteries during thermal runaway. J Electrochem Soc. 2005;152(1):A73–A79.
Smith K, Kim G-H, Darcy E, Pesaran A. Thermal/electrical modeling for abuse-tolerant design of lithium ion modules. Int J Energy Res. 2010;34(2):204–215.
Yang Y, Wang Z, Guo P, Chen S, Bian H, Tong X, Ni L. Carbon oxides emissions from lithium-ion batteries under thermal runaway from measurements and predictive model. J Energy Storage. 2021;33:Article 101863.
Hang T, Mukoyama D, Nara H, Takami N, Momma T, Osaka T. Electrochemical impedance spectroscopy analysis for lithium-ion battery using Li4Ti5O12 anode. J Power Sources. 2013;222:442–447.
Suresh P, Shukla AK, Munichandraiah N. Temperature dependence studies of a.c. impedance of lithium-ion cells. J Appl Electrochem. 2002;32:267–273.
Thomas MGSR, Bruce PG, Goodenough JB. AC impedance analysis of polycrystalline insertion electrodes: Application to Li1-xCoO2. J Electrochem Soc. 1985;132(7):1521–1528.
Zhang SS, Xu K, Jow TR. Electrochemical impedance study on the low temperature of Li-ion batteries. Electrochim Acta. 2004;49(7):1057–1061.
Srinivasan R, Carkhuff BG, Butler MH, Baisden AC. Instantaneous measurement of the internal temperature in lithium-ion rechargeable cells. Electrochim Acta. 2011;56(17):6198–6204.
Srinivasan R, Demirev PA, Carkhuff BG. Rapid monitoring of impedance phase shifts in lithium-ion batteries for hazard prevention. J Power Sources. 2018;405:30–36.
Schmidt J, Arnold S, Loges A, Werner D, Wetzel T, Ivers-Tiffée E. Measurement of the internal cell temperature via impedance: Evaluation and application of a new method. J Power Sources. 2013;243:110–117.
Spinner NS, Love CT, Rose-Pehrsson SL, Tuttle SG. Expanding the operational limits of the single-point impedance diagnostic for internal temperature monitoring of lithium-ion batteries. Electrochim Acta. 2015;174:488–493.
Raijmakers LHJ, Danilov DL, van Lammeren JPM, Lammers MJG, Notten PHL. Sensorless battery temperature measurements based on electrochemical impedance spectroscopy. J Power Sources. 2014;247:539–544.
Raijmakers LHJ, Danilov DL, van Lammeren JPM, Lammers TJG, Bergveld HJ, Notten PHL. Non-zero intercept frequency: An accurate method to determine the integral temperature of Li-ion batteries. IEEE Trans Ind Electron. 2016;63(5):3168–3178.
Dong P, Liu Z, Wu P, Li Z, Wang Z, Zhang J. Reliable and early warning of lithium-ion battery thermal runaway based on electrochemical impedance spectrum. J Electrochem Soc. 2021;168(9):Article 090529.
Carkhuff BG, Demirev PA, Srinivasan R. Impedance-based battery management system for safety monitoring of lithium-ion batteries. IEEE Trans Ind Electron. 2018;65(8):6497–6504.
Lyu N, Jin Y, Xiong R, Miao S, Gao J. Real-time overcharge warning and early thermal runaway prediction of Li-ion battery by online impedance measurement. IEEE Trans Ind Electron. 2022;69(2):1929–1936.
Gao W, Li X, Ma M, Fu Y, Jiang J, Mi C. Case study of an electric vehicle battery thermal runaway and online internal short-circuit detection. IEEE Trans Power Electron. 2021;36(3):2452–2455.
Sun J, Wei G, Pei L, Lu R, Song K, Wu C, Zhu C. Online internal temperature estimation for lithium-ion batteries based on kalman filter. Energies. 2015;8(5):4400–4415.
Hinton G, Maaten L. Viualizing data using t-SNE. J Mach Learn Res. 2008;9(2605):2579–2605.
Badrinarayanan R, Zhao J, Tseng KJ, Skyllas-Kazacos M. Extended dynamic model for ion diffusion in all-vanadium redox flow battery including the effects of temperature and bulk electrolyte transfer. J Power Sources. 2014;270:576–586.
Zhang Y, Zhao J, Wang P, Skyllas-Kazacos M, Xiong B, Badrinarayanan R. A comprehensive equivalent circuit model of all-vanadium redox flow battery for power system analysis. J Power Sources. 2015;290:14–24.
Shannon C, Weaver W. The mathematical theory of communication. Urbana, Chicago, Springfield (IL): University of Illinois Press: 1949.
Miśkiewicz J. Improving quality of sample entropy estimation for continuous distribution probability functions. Physica A. 2016;450:473–485.
Wang Z, Liu P, Wang Z. Voltage fault diagnosis and prognosis of battery systems based on entropy and Z -score for electric vehicles. Appl Energy. 2017;196:289–302.
Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA. 1991;88:2297–2301.
Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278(6):2039–2049.
Hong J, Wang Z, Liu P. Big-data-based thermal runaway prognosis of battery systems for electric vehicles. Energies. 2017;10(7):919.
Singh A, Izadian A, Anwar S. Model based condition monitoring in lithium-ion batteries. J Power Sources. 2014;268:459–468.
Xia B, Shang Y, Nguyen T, Mi C. A correlation based fault detection method for short circuits in battery packs. J Power Sources. 2017;337:1–10.
Xia B, Nguyen T, Yang J, Mi C. The improved interleaved voltage measurement method for series connected battery packs. J Power Sources. 2016;334:12–22.
Sahraei E, Campbell J, Wierzbicki T. Modeling and short circuit detection of 18650 Li-ion cells under mechanical abuse conditions. J Power Sources. 2012;220:360–372.
Fang W, Ramadass P, Zhang Z(J). Study of internal short in a Li-ion cell-Ⅱ. Numerical investigation using a 3D electrochemical-thermal model. J Power Sources. 2014;248:1090–1098.
Maleki H, Howard JN. Internal short circuit in Li-ion cells. J Power Sources. 2009;191(2):568–574.
Hatchard TD, Trussler S, Dahn JR. Building a “smart nail” for penetration tests on Li-ion cells. J Power Sources. 2014;247:821–823.
Orendorff CJ, Peter Roth E, Nagasubramanian G. Experimental triggers for internal short circuits in lithium-ion cells. J Power Sources. 2011;196(15):6554–6558.
Santhanagopalan S, Ramadass P, Zhang J(Z). Analysis of internal short-circuit in a lithium ion cell. J Power Sources. 2009;194(1):550–557.
Feng X, Fang M, He X, Ouyang M, Lu L, Wang H, Zhang M. Thermal runaway features of large format prismatic lithium ion battery using extended volume accelerating rate calorimetry. J Power Sources. 2014;255:294–301.
Yang R, Xiong R, He H, Chen Z. A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application. J Clean Prod. 2018;187:950–959.
Cai J. Applying support vector machine to predict the critical heat flux in concentric-tube open thermosiphon. Ann Nucl Energy. 2012;43:114–122.
Patil MA, Tagade P, Hariharan KS, Kolake SM, Song T, Yeo T, Doo S. A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation. Appl Energy. 2015;159:285–297.
Wang C, Li C, Wang G, Zhang C, Cui N. Fast identification method for thermal model parameters of Lithium-ion battery based on discharge temperature rise. J Energy Storage. 2021;44:Article 103362.
Chen D, Xiao L, Yan W, Guo Y. A novel hybrid equivalent circuit model for lithium-ion battery considering nonlinear capacity effects. Energy Rep. 2021;7:320–329.
Lin X, Tang Y, Ren J, Wei Y. State of charge estimation with the adaptive unscented Kalman filter based on an accurate equivalent circuit model. J Energy Storage. 2021;41:Article 102840.
Hong J, Wang Z, Yao Y. Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks. Appl Energy. 2019;251:113381.
Ding S, Dong C, Zhao T, Koh L, Bai X, Luo J. A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting. IEEE Trans Industr Inform. 2021;17(7):4503–4511.
Hospedales T, Antoniou A, Micaelli P, Storkey A. Meta-learning in neural networks: A survey. IEEE Trans Pattern Anal Mach Intell. 2022;44(9):5149–5169.
Robinson JB, Darr JA, Eastwood DS, Hinds G, Lee PD, Shearing PR, Taiwo OO, DJL B. Non-uniform temperature distribution in Li-ion batteries during discharge – A combined thermal imaging, X-ray micro-tomography and electrochemical impedance approach. J Power Sources. 2014;252:51–57.
京公网安备11010802044758号