Abstract
The trend of electrification within the automotive industry has resulted in the development of electric vehicles (EVs) due to their sustainable and environment-friendly feature. One of the major components of these cars is their energy storage device, as its performance is very crucial for the success of the EV. But the major concern is the properties of the energy storage or battery change with time and usage. Therefore, the degradation of battery properties is interesting, especially the capacity decline. To understand and counter this degradation, it must be measured with high precision. This paper develops a data-driven approach to estimate the health of the batteries by using the support vector machine model. The proposed method identifies the keen points of the operation of an EV battery under aging conditions by performing accelerated aging and trains them with the adapted data-driven approach. To access the aging conditions, an equivalent electrical model of a cell of EV battery is simulated and operated for various current pulses. Further, when tested with unkown operation of a battery, the developed estimator predicted its health with an accuracy of 96.25%. The results depicted that the proposed concept gives an improved measure of the battery state and a pattern on how the capacity degradation occurs with time.
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References
Ritchie AG (2004) Recent developments and likely advances in lithium rechargeable batteries. J Power Sources 136:285–289. https://doi.org/10.1016/j.jpowsour.2004.03.013
Yang F, Wang D, Zhao Y et al (2018) A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries. Energy 145:486–495. https://doi.org/10.1016/j.energy.2017.12.144
Barré A, Deguilhem B, Grolleau S et al (2013) A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. J Power Sources 241:680–689. https://doi.org/10.1016/j.jpowsour.2013.05.040
Hesse HC, Schimpe M, Kucevic D, Jossen A (2017) Lithium-ion battery storage for the grid—a review of stationary battery storage system design tailored for applications in modern power grids. Energies 10(12):2107
Barai A, Uddin K, Dubarry M et al (2019) A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells. Prog Energy Combust Sci 72:1–31. https://doi.org/10.1016/j.pecs.2019.01.001
Xu B, Oudalov A, Ulbig A et al (2018) Modeling of lithium-ion battery degradation for cell life assessment. IEEE Trans Smart Grid 9:1131–1140. https://doi.org/10.1109/TSG.2016.2578950
Todeschini F, Onori S, Rizzoni G (2012) An experimentally validated capacity degradation model for Li-ion batteries in PHEVs applications. IFAC Proc 45:456–461. https://doi.org/10.3182/20120829-3-MX-2028.00173
Tomaszewska A, Chu Z, Feng X et al (2019) Lithium-ion battery fast charging: a review. eTransportation 1:100011. https://doi.org/10.1016/j.etran.2019.100011
Vetter J, Novák P, Wagner MR et al (2005) Ageing mechanisms in lithium-ion batteries. J Power Sources 147:269–281. https://doi.org/10.1016/j.jpowsour.2005.01.006
Han X, Ouyang M, Lu L et al (2014) A comparative study of commercial lithium ion battery cycle life in electrical vehicle: aging mechanism identification. J Power Sources 251:38–54. https://doi.org/10.1016/j.jpowsour.2013.11.029
Agubra V, Fergus J (2013) Lithium ion battery anode aging mechanisms. Materials (Basel) 6:1310–1325. https://doi.org/10.3390/ma6041310
Wang J, Purewal J, Liu P et al (2014) Degradation of lithium ion batteries employing graphite negatives and nickel–cobalt–manganese oxide + spinel manganese oxide positives: part 1, aging mechanisms and life estimation. J Power Sources 269:937–948. https://doi.org/10.1016/j.jpowsour.2014.07.030
Danzer MA, Liebau V, Maglia F (2015) Aging of lithium-ion batteries for electric vehicles. In: Advances in battery technologies for electric vehicles. Elsevier, pp 359–387
Waag W, Fleischer C, Sauer DU (2014) Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J Power Sources 258:321–339. https://doi.org/10.1016/j.jpowsour.2014.02.064
Lu L, Han X, Li J et al (2013) A review on the key issues for lithium-ion battery management in electric vehicles. J Power Sources 226:272–288. https://doi.org/10.1016/j.jpowsour.2012.10.060
Bloom I, Cole B, Sohn J et al (2001) An accelerated calendar and cycle life study of Li-ion cells. J Power Sources 101:238–247. https://doi.org/10.1016/S0378-7753(01)00783-2
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1023/A:1022627411411
Mercer J (1909) Functions of positive and negative type, and their connection with the theory of integral equations. Philos Trans R Soc A Math Phys Eng Sci 209:415–446. https://doi.org/10.1098/rsta.1909.0016
Chapelle O (2007) Training a support vector machine in the primal. Neural Comput 19:1155–1178. https://doi.org/10.1162/neco.2007.19.5.1155
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Singh, R., Kurukuru, V.S.B., Khan, M.A. (2021). Data-Driven Model for State of Health Estimation of Lithium-Ion Battery. In: Singh, V., Asari, V.K., Kumar, S., Patel, R.B. (eds) Computational Methods and Data Engineering. Advances in Intelligent Systems and Computing, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7907-3_21
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DOI: https://doi.org/10.1007/978-981-15-7907-3_21
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