Abstract
Electric and hybrid electric vehicles are becoming more popular today. Typically, batteries serve as the major energy source. Battery management is used to optimize battery use and protection. This battery management system provides cell balancing and guards against overcharging and over-discharging of batteries. For these purposes, a precise state of charge assessment is required. The many techniques used to determine state of charge (SOC) can be categorized as direct measurement techniques, accounting techniques, adaptive techniques, and hybrid techniques. This article discusses the benefits and drawbacks of the most prominent state-of-charge estimation methodologies. The review also outlines the critical reaction factors required for calculating the battery SOC precisely. This will help make sure that the SOC assessment is precise. It will help a lot when deciding on the best method for making an EV's energy storageĀ and control strategy secure and reliable.
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References
Bilgin B, Magne P, Malysz P, Yang Y, Pantelic V, Preindl M, Korobkine A, Jiang W, Lawford M, Emadi A (2015) Making the case for electrified transportation. IEEE Trans Transp Electrif 1:4ā17
Xiong R, He H, Sun F, Zhao K (2012) Online estimation of peak power capability of Li-Ion batteries in electric vehicles by a hardware-in-loop approach. Energies 5(5):1455ā1469
Xing Y, Ma EWM, Tsui KL, Pecht M (2011) Battery management systems in electric and hybrid vehicles. Energies 4(11):1840ā1857
Zhang C, Wang LY, Li X, Chen W, Yin GG, Jiang J (2015) āRobust and adaptive estimation of state of charge for lithium-ion batteries.ā IEEE Trans Ind Electron 62(8):4948ā4957
Hu X, Li SE, Yang Y (2016) āAdvanced machine learning approach for lithium-ion battery state estimation in electric vehicles.ā IEEE Trans Transp Electrif 2(2):140ā149
Xiong R, Zhang Y, He H, Zhou X, Pecht MG (2018) āA doublescale, particle-filtering, energy state prediction algorithm for lithium-ion batteries.ā IEEE Trans Ind Electron 65(2):1526ā1538
Xiong R, Yu Q, Wang LY, Lin C (2017) A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter. Appl Energy 207:341ā348
Scacchioli, Rizzoni G, Salman MA, Li W, Onori S, Zhang X (2014) āModel-based diagnosis of an automotive electric power generation and storage system. IEEE Trans Syst Man Cybern Syst 44(1):72ā85
Lu L, Han X, Li J, Hua J, Ouyang M (2013) āA review on the key issues for lithium-ion battery management in electric vehicles.ā J Power Sour 226:272ā288
Campestrini C, Horsche MF, Zilberman I, Heil T, Zimmermann T, Jossen A (2016) Validation and benchmark methods for battery management system functionalities: state of charge estimation algorithms. J Energy Storage 7:38ā51
Chen Z, Xia B, You C, Mi CC (2015) A novel energy management method for series plugin hybrid electric vehicles. Appl Energy 145:172ā179
Chaoui H, Golbon N, Hmouz I, Souissi R, Tahar S (2014) Lyapunov-based adaptive state of charge and state of health estimation for lithium-ion batteries. IEEE Trans Ind Electron 62:1610ā1618
Lu L, Han X, Li J, Hua J, Ouyang M (2013) A review on the key issues for lithium-ion battery management in electric vehicles. J Power Sources 226:272ā288
Sun F, Xiong R, He H (2016) A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Appl Energy 162:1399ā1409
Marcial-Simon E, Mehlhorn R (2019) Method for controlling electrical charging of a group of vehicles. U.S. Patent 20190168633A1, 6 June 2019
Nobili F, Mancini M, Dsoke S, Tossici R, Marassi R, Low-temperature behavior of graphiteātin composite anodes for Li-ion batteries. J Power Sources 195:7090ā7097.
Lillehei CW, Cruz AB, Johnsrude I, Sellers RD (1965) A new method of assessing the state of charge of implanted cardiac pacemaker batteries. Am J Cardiol 16:717ā721
MIT Electric Vehicle Team (2008) A guide to understanding battery specifications, December 2008. http://mit.edu/evt/summary_battery_specifications.pdf
Chiasson J, Vairamohan B (2003) Estimating the state of charge of a battery. In: Proceedings of the 2003 American control conference, vol 4, no 2, pp 2863ā2868
Wang NC, Hun, Yan Q (2011) Research on state of charge estimation of batteries used in electric vehicle. In: Asia-Pacific power energy engineering conference, pp 1ā4
Kassim MRM, Jamil WAW, Sabri RM (2021) State-of-Charge (SOC) and State-of-Health (SOH) estimation methods in battery management systems for electric vehicles, pp 91ā96
Zhang S, Guo X, Dou X, Zhang X (2020) A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery. Sustain Energy Technol Assess 40:100752
Xia G, Huang Y, Li F et al (2020) A thermally flexible and multi-site tactile sensor for remote 3D dynamic sensing imaging. Front Chem Sci Eng 14(6):1039ā1051
Wang HH, Liu YY (2013) Estimation of state of charge of batteries for electric vehicles. Int J Control Autom 6:185ā194
Anbuky H, Pascoe PE (2000) VRLA battery state-of charge estimation in telecommunication power systems. IEEE Trans Industr Electron 47(3):565ā573
Zheng L, Zhang L, Zhu J, Wang G, Jiang J (2016) Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model. Appl Energy 180:424ā434
Feng X, Zhang Y, Kang L et al (2020) Integrated energy storage system based on tribo electric nano generator in electronic devices. Front Chem Sci Eng 15(2):238ā250
Hua Y, Wang N, Zhao K (2021) Simultaneous unknown input and state estimation for the linear system with a rank-deficient distribution matrix.Ā Math Probl Eng 2021, Article ID 6693690, 11pp
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Singh, A., Yadav, A. (2024). Overview of SOC Estimation Strategies for Battery Management in Electric Vehicles. In: Murari, K., Singh, B., Sood, V.K. (eds) Recent Advances in Power Electronics and Drives. EPREC 2023. Lecture Notes in Electrical Engineering, vol 1139. Springer, Singapore. https://doi.org/10.1007/978-981-99-9439-7_14
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DOI: https://doi.org/10.1007/978-981-99-9439-7_14
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