IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i13p3436-d1691495.html
   My bibliography  Save this article

Optimization and Estimation of the State of Charge of Lithium-Ion Batteries for Electric Vehicles

Author

Listed:
  • Luc Vivien Assiene Mouodo

    (Department of Electrical Engineering, Higher Normal School of Technical Education (ENSET), University of Douala, Douala P.O. Box 1872, Cameroon
    Department of Mechanical Engineering, University of West Attica, Campus II, Thivon 250, 12 241 Aegaleo, Greece
    Department of Electrical Engineering, University Institute of Technology (IUT) of Douala, Douala P.O. Box 8698, Cameroon)

  • Petros J. Axaopoulos

    (Department of Mechanical Engineering, University of West Attica, Campus II, Thivon 250, 12 241 Aegaleo, Greece)

Abstract

Lithium batteries have become one of the best choices for current consumer electric vehicle batteries due to their good stability and high energy density. To ensure the safety and reliability of electric vehicles (EVs), the battery management system (BMS) must provide accurate and real-time information on the usage status of the onboard battery. This article highlights the precise estimation of the state of charge (SOC) applied to four models of lithium-ion batteries (Turnigy, LG, SAMSUNG, and PANASONIC) for electric vehicles in order to ensure optimal use of the battery and extend its lifespan, which is frequently influenced by certain parameters such as temperature, current, number of charge and discharge cycles, and so on. Because of the work’s novelty, the methodological approach combines the extended Kalman filter algorithm (EKF) with the noise matrix, which is updated in this case through an iterative process. This leads to the migration to a new adaptive extended Kalman filter algorithm (AEKF) in the MATLAB Simulink 2022.b environment, which is novel or original in the sense that it has a first-order association. The four models of batteries from various manufacturers were directly subjected to the Venin estimator, which allowed for direct comparison of the models under a variety of temperature scenarios while keeping an eye on performance metrics. The results obtained were mapped charging status (SOC) versus open circuit voltage (OCV), and the high-performance primitives collection (HPPC) tests were carried out at 40 °C, 25 °C, 10 °C, 0 °C and −10 °C. At these temperatures, their corresponding values for the root mean square error (RMSE) of (SOC) for the Turnigy graphene battery model were found to be: 1.944, 9.6237, 1.253, 1.6963, 16.9715, and for (OCV): 1.3154, 4.895, 4.149, 4.1808, and 17.2167, respectively. The tests cover the SOC range, from 100% to 5% with four different charge and discharge currents at rates of 1, 2, 5 and 10 A. After characterization, the battery was subjected to urban dynamometer driving program (UDDS), Energy Saving Test (HWFET) driving cycles, LA92 (Dynamometric Test), US06 (aggressive driving), as well as combinations of these cycles. Driving cycles were sampled every 0.1 s, and other tests were sampled at a slower or variable frequency, thus verifying the reliability and robustness of the estimator to 97%.

Suggested Citation

  • Luc Vivien Assiene Mouodo & Petros J. Axaopoulos, 2025. "Optimization and Estimation of the State of Charge of Lithium-Ion Batteries for Electric Vehicles," Energies, MDPI, vol. 18(13), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3436-:d:1691495
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/13/3436/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/13/3436/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gong, Dongliang & Gao, Ying & Kou, Yalin & Wang, Yurang, 2022. "State of health estimation for lithium-ion battery based on energy features," Energy, Elsevier, vol. 257(C).
    2. Zhenhua Cui & Jiyong Dai & Jianrui Sun & Dezhi Li & Licheng Wang & Kai Wang & A. M. Bastos Pereira, 2022. "Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
    3. Shen, Yanqing, 2018. "Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles," Energy, Elsevier, vol. 152(C), pages 576-585.
    4. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    5. Zahid, Taimoor & Xu, Kun & Li, Weimin & Li, Chenming & Li, Hongzhe, 2018. "State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles," Energy, Elsevier, vol. 162(C), pages 871-882.
    6. Radoslav S. Dimitrov, 2016. "The Paris Agreement on Climate Change: Behind Closed Doors," Global Environmental Politics, MIT Press, vol. 16(3), pages 1-11, August.
    7. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    8. Tang, Xiaopeng & Gao, Furong & Zou, Changfu & Yao, Ke & Hu, Wengui & Wik, Torsten, 2019. "Load-responsive model switching estimation for state of charge of lithium-ion batteries," Applied Energy, Elsevier, vol. 238(C), pages 423-434.
    9. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    10. Peng, Jiankun & Luo, Jiayi & He, Hongwen & Lu, Bing, 2019. "An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    11. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    12. Xia, Bizhong & Chen, Chaoren & Tian, Yong & Wang, Mingwang & Sun, Wei & Xu, Zhihui, 2015. "State of charge estimation of lithium-ion batteries based on an improved parameter identification method," Energy, Elsevier, vol. 90(P2), pages 1426-1434.
    13. Zhai, Xiangyu & Li, Zening & Li, Zhengmao & Xue, Yixun & Chang, Xinyue & Su, Jia & Jin, Xiaolong & Wang, Peng & Sun, Hongbin, 2025. "Risk-averse energy management for integrated electricity and heat systems considering building heating vertical imbalance: An asynchronous decentralized approach," Applied Energy, Elsevier, vol. 383(C).
    14. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    15. Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.
    16. Bockrath, Steffen & Lorentz, Vincent & Pruckner, Marco, 2023. "State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles," Applied Energy, Elsevier, vol. 329(C).
    17. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    18. Zheng, Fangdan & Xing, Yinjiao & Jiang, Jiuchun & Sun, Bingxiang & Kim, Jonghoon & Pecht, Michael, 2016. "Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 183(C), pages 513-525.
    19. Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    2. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    3. Wang, Jianfeng & Zuo, Zhiwen & Wei, Yili & Jia, Yongkai & Chen, Bowei & Li, Yuhan & Yang, Na, 2024. "State of charge estimation of lithium-ion battery based on GA-LSTM and improved IAKF," Applied Energy, Elsevier, vol. 368(C).
    4. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    5. Vichard, L. & Ravey, A. & Venet, P. & Harel, F. & Pelissier, S. & Hissel, D., 2021. "A method to estimate battery SOH indicators based on vehicle operating data only," Energy, Elsevier, vol. 225(C).
    6. Stefano Leonori & Luca Baldini & Antonello Rizzi & Fabio Massimo Frattale Mascioli, 2021. "A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells," Energies, MDPI, vol. 14(21), pages 1-29, November.
    7. Singh, Karanjot & Tjahjowidodo, Tegoeh & Boulon, Loïc & Feroskhan, Mir, 2022. "Framework for measurement of battery state-of-health (resistance) integrating overpotential effects and entropy changes using energy equilibrium," Energy, Elsevier, vol. 239(PA).
    8. Sun, Li & Li, Guanru & You, Fengqi, 2020. "Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    9. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    10. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    11. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    12. Wang, Tong & Wu, Yan & Zhu, Keming & Cen, Jianmeng & Wang, Shaohong & Huang, Yuqi, 2025. "Deep learning and polarization equilibrium based state of health estimation for lithium-ion battery using partial charging data," Energy, Elsevier, vol. 317(C).
    13. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    14. Chen, Bingyang & Zeng, Xingjie & Liu, Chao & Xu, Yafei & Cao, Heling, 2025. "Health management of power batteries in low temperatures based on Adaptive Transfer Enformer framework," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    15. Zafar, Muhammad Hamza & Mansoor, Majad & Abou Houran, Mohamad & Khan, Noman Mujeeb & Khan, Kamran & Raza Moosavi, Syed Kumayl & Sanfilippo, Filippo, 2023. "Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles," Energy, Elsevier, vol. 282(C).
    16. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
    17. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    18. Kuo Yang & Yugui Tang & Zhen Zhang, 2021. "Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter," Energies, MDPI, vol. 14(4), pages 1-15, February.
    19. Wang, Chao & Wang, Xin & Yang, Mingjian & Li, Jiale & Qian, Feng & Zhang, Zunhua & Zhou, Mengni & Guo, Xiaofeng & Wang, Kai, 2025. "Concurrent estimation of lithium-ion battery charge and energy states by fractional-order model and multi-innovation adaptive cubature Kalman filter," Energy, Elsevier, vol. 322(C).
    20. Wu, Chunling & Hu, Wenbo & Meng, Jinhao & Xu, Xianfeng & Huang, Xinrong & Cai, Lei, 2023. "State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment," Energy, Elsevier, vol. 274(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3436-:d:1691495. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.