A comprehensive review of state of charge estimation in lithium-ion batteries used in electric vehicles

被引:90
作者
Selvaraj, Vedhanayaki [1 ]
Vairavasundaram, Indragandhi [1 ]
机构
[1] VIT, Sch Elect Engn, Vellore, India
关键词
Electric vehicle; Lithium-ion battery; Battery management system; State of charge; Direct methods; Model-based methods; Observer-based methods; Filter-based methods; Data-driven methods; EXTENDED KALMAN FILTER; GATED RECURRENT UNIT; SLIDING MODE OBSERVER; NEURAL-NETWORK MODEL; REAL-TIME STATE; OF-CHARGE; SOC ESTIMATION; PARTICLE FILTER; MANAGEMENT-SYSTEMS; HEALTH ESTIMATION;
D O I
10.1016/j.est.2023.108777
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
In the recent era, Electric Vehicles (EVs) has been emerged as the top concern in the automobile sector because of their eco-friendly nature. The application of Lithium-ion batteries as an energy storage device in EVs is considered the best solution due to their high energy density, less weight, and high specific power density. The battery management system plays a significant part in ensuring the safety and reliability of lithium-ion batteries. The State of Charge (SOC) acts as the performance indicator of the battery. Incorrect estimation of SOC leads to overcharging or over-discharging. SOC estimation has become a challenging task due to the considerable changes occurring in battery characteristics due to the aging effect, temperature effect, and existence of non-linear characteristics of the battery. In recent years, estimation of SOC has become an active research area. Many researchers strive to develop a novel SOC estimation method that is highly robust, accurate, and has low complexity in implementation and computation. In this paper, a comprehensive review of various SOC estimation methods, including direct methods, model-based methods, observer-based methods, filter-based methods, and data-driven methods, were summarized. A detailed discussion on the benefits and drawbacks of each method was held. Finally, future aspects in SOC estimation for developing novel estimation methods were presented.
引用
收藏
页数:25
相关论文
共 192 条
[1]   Rapid test and non-linear model characterisation of solid-state lithium-ion batteries [J].
Abu-Sharkh, S ;
Doerffel, D .
JOURNAL OF POWER SOURCES, 2004, 130 (1-2) :266-274
[2]   Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework [J].
Ahmadian, Ali ;
Sedghi, Mahdi ;
Elkamel, Ali ;
Fowler, Michael ;
Golkar, Masoud Aliakbar .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 81 :2609-2624
[3]   Battery state-of-charge estimator using the SVM technique [J].
Alvarez Anton, J. C. ;
Garcia Nieto, P. J. ;
de Cos Juez, F. J. ;
Sanchez Lasheras, F. ;
Gonzalez Vega, M. ;
Roqueni Gutierrez, M. N. .
APPLIED MATHEMATICAL MODELLING, 2013, 37 (09) :6244-6253
[4]  
Amir U, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON ELECTRICAL SYSTEMS FOR AIRCRAFT, RAILWAY, SHIP PROPULSION AND ROAD VEHICLES & INTERNATIONAL TRANSPORTATION ELECTRIFICATION CONFERENCE (ESARS-ITEC)
[5]   Accuracy improvement of SOC estimation in lithium-ion batteries [J].
Awadallah, Mohamed A. ;
Venkatesh, Bala .
JOURNAL OF ENERGY STORAGE, 2016, 6 :95-104
[6]  
Azis NA, 2019, INT CONF INSTRUM, P88, DOI [10.1109/ica.2019.8916734, 10.1109/ICA.2019.8916734]
[7]   Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity [J].
Bashash, Saeid ;
Moura, Scott J. ;
Forman, Joel C. ;
Fathy, Hosam K. .
JOURNAL OF POWER SOURCES, 2011, 196 (01) :541-549
[8]  
BENF, 2019, Energy Storage Outlook 2019
[9]   Estimating State of Charge for xEV Batteries Using 1D Convolutional Neural Networks and Transfer Learning [J].
Bhattacharjee, Arnab ;
Verma, Ashu ;
Mishra, Sukumar ;
Saha, Tapan K. .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 70 (04) :3123-3135
[10]   Robust state-of-charge estimation of Li-ion batteries based on multichannel convolutional and bidirectional recurrent neural networks [J].
Bian, Chong ;
Yang, Shunkun ;
Liu, Jie ;
Zio, Enrico .
APPLIED SOFT COMPUTING, 2022, 116