Review of Management System and State-of-Charge Estimation Methods for Electric Vehicles

被引:5
|
作者
Sarda, Jigar [1 ]
Patel, Hirva [2 ]
Popat, Yashvi [3 ]
Hui, Kueh Lee [4 ]
Sain, Mangal [5 ]
机构
[1] Charotar Univ Sci & Technol, Chandubhai S Patel Inst Technol, M&V Patel Dept Elect Engn, Changa 388421, India
[2] Pandit Deendayal Energy Univ, Sch Technol, Dept Informat & Commun Technol, Gandhinagar 382007, India
[3] Charotar Univ Sci & Technol, Devang Patel Inst Adv Technol & Res, Comp Engn, Changa 388421, India
[4] Dong A Univ, Dept Elect Engn, Busan 49236, South Korea
[5] Div Comp & Informat Engn, Busan 49236, South Korea
来源
WORLD ELECTRIC VEHICLE JOURNAL | 2023年 / 14卷 / 12期
关键词
battery management system; SOC estimation; Kalman filter method; deep learning method; LITHIUM-ION BATTERIES; SOC ESTIMATION; LIFEPO4; BATTERIES; ONLINE ESTIMATION; ACCURATE STATE; MODEL; NETWORK; HYBRID; HEALTH; OBSERVER;
D O I
10.3390/wevj14120325
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Energy storage systems (ESSs) are critically important for the future of electric vehicles. Due to the shifting global environment for electrical distribution and consumption, energy storage systems (ESS) are amongst the electrical power system solutions with the fastest growing market share. Any ESS must have the capacity to regulate the modules from the system in the case of abnormal situations as well as the ability to monitor, control, and maximize the performance of one or more battery modules. Such a system is known as a battery management system (BMS). One parameter that is included in the BMS is the state-of-charge (SOC) of the battery. The BMS is used to enhance battery performance while including the necessary safety measures in the system. SOC estimation is a key BMS feature, and precise modelling and state estimation will improve stable operation. This review discusses the current methods used in BEV LIB SOC modelling and estimation. It also efficiently monitors all of the electrical characteristics of a battery-pack system, including the voltage, current, and temperature. The main function of a BMS is to safeguard a battery system for machine electrification and electric propulsion. The major responsibility of the BMS is to guarantee the trustworthiness and safety of the battery cells coupled to create high currents at high voltage levels. This article examines the advancements and difficulties in (i) cutting-edge battery technology and (ii) cutting-edge BMS for electric vehicles (EVs). This article's main goal is to outline the key characteristics, benefits and drawbacks, and recent technological developments in SOC estimation methods for a battery. The study follows the pertinent industry standards and addresses the functional safety component that concerns BMS. This information and knowledge will be valuable for vehicle manufacturers in the future development of new SOC methods or an improvement in existing ones.
引用
收藏
页数:33
相关论文
共 50 条
  • [31] Estimation of state of charge of batteries for electric vehicles
    Wang, H. (why.69@163.com), 1600, Science and Engineering Research Support Society, 20 Virginia Court, Sandy Bay, Tasmania, Australia (06):
  • [32] Review on the State of Charge Estimation Methods for Electric Vehicle Battery
    Zhang, Mingyue
    Fan, Xiaobin
    WORLD ELECTRIC VEHICLE JOURNAL, 2020, 11 (01):
  • [33] Estimation of battery state-of-charge for electric vehicles using an MCMC-based auxiliary particle filter
    Cai, Wei
    Wang, Jun
    2016 AMERICAN CONTROL CONFERENCE (ACC), 2016, : 4018 - 4021
  • [34] A Smartphone-in-the-Loop Active State-of-Charge Manager for Electric Vehicles
    Dardanelli, Andrea
    Tanelli, Mara
    Picasso, Bruno
    Savaresi, Sergio M.
    di Tanna, Onorino
    Santucci, Mario Donato
    IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2012, 17 (03) : 454 - 463
  • [35] State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach
    How, Dickshon N. T.
    Hannan, Mahammad A.
    Lipu, Molla S. Hossain
    Sahari, Khairul S. M.
    Ker, Pin Jern
    Muttaqi, Kashem M.
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2020, 56 (05) : 5565 - 5574
  • [36] State-of-Charge Estimation of Li-ion Battery in Electric Vehicles: A Deep Neural Network Approach
    How, Dickson N. T.
    Hannan, M. A.
    Lipu, M. S. Hossain
    Sahari, K. S. M.
    Ker, P. J.
    Muttaqi, K. M.
    2019 IEEE INDUSTRY APPLICATIONS SOCIETY ANNUAL MEETING, 2019,
  • [37] State-of-Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Deep Neural Network
    Premkumar, M.
    Sowmya, R.
    Sridhar, S.
    Kumar, C.
    Abbas, Mohamed
    Alqahtani, Malak S.
    Nisar, Kottakkaran Sooppy
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 73 (03): : 6289 - 6306
  • [38] Techniques for Estimating the State-of-Charge of Lead Acid Batteries in Electric Vehicles
    Shiao, Ying-Shing
    Su, Ding-Tsair
    Ko, Chen-Tsai
    Hung, Rong-Wen
    PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON SYSTEMS THEORY AND SCIENTIFIC COMPUTATION (ISTAC'08): NEW ASPECTS OF SYSTEMS THEORY AND SCIENTIFIC COMPUTATION, 2008, : 270 - +
  • [39] Spatio-temporal analysis of state-of-charge streams for electric vehicles
    Lee, Junghoon
    Park, Gyung-Leen
    Cho, Yumin
    Kim, Suna
    Jung, Jiwon
    IPSN'15: PROCEEDINGS OF THE 14TH INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING IN SENSOR NETWORKS, 2015, : 368 - 369
  • [40] A Novel Method for Estimating State-of-Charge in Power Batteries for Electric Vehicles
    Zhang, Nan
    Zhou, Yunshan
    Tian, Qiang
    Liao, Xiaoying
    Zhang, Feitie
    INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2019, 20 (05) : 845 - 852