IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4397-d1469866.html
   My bibliography  Save this article

Enhanced Second-Order RC Equivalent Circuit Model with Hybrid Offline–Online Parameter Identification for Accurate SoC Estimation in Electric Vehicles under Varying Temperature Conditions

Author

Listed:
  • Hao Zhou

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Qiaoling He

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Yichuan Li

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Yangjun Wang

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Dongsheng Wang

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Yongliang Xie

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

Abstract

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency.

Suggested Citation

  • Hao Zhou & Qiaoling He & Yichuan Li & Yangjun Wang & Dongsheng Wang & Yongliang Xie, 2024. "Enhanced Second-Order RC Equivalent Circuit Model with Hybrid Offline–Online Parameter Identification for Accurate SoC Estimation in Electric Vehicles under Varying Temperature Conditions," Energies, MDPI, vol. 17(17), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4397-:d:1469866
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4397/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/17/4397/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    2. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    3. Chen, Laien & Zeng, Xiaoyong & Xia, Xiangyang & Sun, Yaoke & Yue, Jiahui, 2024. "A modeling and state of charge estimation approach to lithium-ion batteries based on the state-dependent autoregressive model with exogenous inputs," Energy, Elsevier, vol. 300(C).
    4. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arsalan Rasoolzadeh & Sayed Amir Hashemi & Majid Pahlevani, 2025. "Online Identification of Differential Order in Supercapacitor Fractional-Order Models: Advancing Practical Implementation," Energies, MDPI, vol. 18(8), pages 1-14, April.

    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. Li, Feng & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & Zhang, Guangde, 2024. "State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model," Energy, Elsevier, vol. 289(C).
    2. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    3. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
    4. Bingzi Cai & Mutian Li & Huawei Yang & Chunsheng Wang & Yougen Chen, 2023. "State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm," Energies, MDPI, vol. 16(23), pages 1-15, November.
    5. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    6. Li, Xiaopeng & Zhao, Minghang & Zhong, Shisheng & Li, Junfu & Fu, Song & Yan, Zhiqi, 2024. "BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator," Energy, Elsevier, vol. 313(C).
    7. Xie, Peilin & Zhou, Fan & Tan, Sen & Liso, Vincenzo & Sahlin, Simon Lennart, 2025. "Development of a two-layer control and management system for a residential microgrid with HT-PEMFC-based micro-CHP," Applied Energy, Elsevier, vol. 381(C).
    8. Ximing Cheng & Liguang Yao & Yinjiao Xing & Michael Pecht, 2016. "Novel Parametric Circuit Modeling for Li-Ion Batteries," Energies, MDPI, vol. 9(7), pages 1-15, July.
    9. Su, Zhenqing & Li, Jiankun & Pang, Qiwei & Su, Miao, 2025. "China futures market and world container shipping economy: An exploratory analysis based on deep learning," Research in International Business and Finance, Elsevier, vol. 76(C).
    10. Yu Cao & Xin Wen & Hongyu Liang, 2024. "Multi-Temperature State-of-Charge Estimation of Lithium-Ion Batteries Based on Spatial Transformer Network," Energies, MDPI, vol. 17(20), pages 1-12, October.
    11. Son, Donghee & Song, Youngbin & Park, Shina & Oh, Junseok & Kim, Sang Woo, 2025. "Online state-of-charge and capacity co-estimation for lithium-ion batteries under aging and varying temperatures," Energy, Elsevier, vol. 316(C).
    12. Benedikt Rzepka & Simon Bischof & Thomas Blank, 2021. "Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide," Energies, MDPI, vol. 14(13), pages 1-17, June.
    13. Wan, Sicheng & Yang, Haojing & Lin, Jinwen & Li, Junhui & Wang, Yibo & Chen, Xinman, 2024. "Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM," Energy, Elsevier, vol. 310(C).
    14. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    15. Xingtao Liu & Chaoyi Zheng & Ji Wu & Jinhao Meng & Daniel-Ioan Stroe & Jiajia Chen, 2020. "An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries," Energies, MDPI, vol. 13(2), pages 1-16, January.
    16. Li, Zongxiang & Li, Liwei & Chen, Jing & Wang, Dongqing, 2024. "A multi-head attention mechanism aided hybrid network for identifying batteries’ state of charge," Energy, Elsevier, vol. 286(C).
    17. Liu, Zheng & Zhao, Zhenhua & Qiu, Yuan & Jing, Benqin & Yang, Chunshan & Wu, Huifeng, 2023. "Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction," Energy, Elsevier, vol. 283(C).
    18. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    19. Wenhui Zheng & Bizhong Xia & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer," Energies, MDPI, vol. 12(13), pages 1-14, June.
    20. Ashique, Ratil H. & Salam, Zainal & Bin Abdul Aziz, Mohd Junaidi & Bhatti, Abdul Rauf, 2017. "Integrated photovoltaic-grid dc fast charging system for electric vehicle: A review of the architecture and control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1243-1257.

    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:17:y:2024:i:17:p:4397-:d:1469866. 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.