IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4644-d605733.html
   My bibliography  Save this article

A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles

Author

Listed:
  • Bo Huang

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
    Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China)

  • Yuting Ma

    (Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China
    School of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong 643000, China)

  • Chun Wang

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
    Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China)

  • Yongzhi Chen

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China)

  • Quanqing Yu

    (School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China)

Abstract

The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy.

Suggested Citation

  • Bo Huang & Yuting Ma & Chun Wang & Yongzhi Chen & Quanqing Yu, 2021. "A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4644-:d:605733
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4644/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4644/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    2. Herrera, Victor & Milo, Aitor & Gaztañaga, Haizea & Etxeberria-Otadui, Ion & Villarreal, Igor & Camblong, Haritza, 2016. "Adaptive energy management strategy and optimal sizing applied on a battery-supercapacitor based tramway," Applied Energy, Elsevier, vol. 169(C), pages 831-845.
    3. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    4. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    5. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    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. Lin, Wei-Jen & Chen, Kuo-Ching, 2022. "Evolution of parameters in the Doyle-Fuller-Newman model of cycling lithium ion batteries by multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
    2. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo & Xie, Jiale, 2022. "A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry," Energy, Elsevier, vol. 239(PC).
    3. Liu, Jiahao & Fan, Yining & Wang, Jinhui & Tao, Changfa & Chen, Mingyi, 2022. "A model-scale experimental and theoretical study on a mineral oil-immersed battery cooling system," Renewable Energy, Elsevier, vol. 201(P1), pages 712-723.
    4. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    5. Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
    6. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    7. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    8. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    9. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    10. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    11. Chen, Zeyu & Zhang, Bo & Xiong, Rui & Shen, Weixiang & Yu, Quanqing, 2021. "Electro-thermal coupling model of lithium-ion batteries under external short circuit," Applied Energy, Elsevier, vol. 293(C).
    12. 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.
    13. 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.
    14. Tian, Yong & Dong, Qianyuan & Tian, Jindong & Li, Xiaoyu & Li, Guang & Mehran, Kamyar, 2023. "Capacity estimation of lithium-ion batteries based on optimized charging voltage section and virtual sample generation," Applied Energy, Elsevier, vol. 332(C).
    15. Lin, Mingqiang & Yan, Chenhao & Wang, Wei & Dong, Guangzhong & Meng, Jinhao & Wu, Ji, 2023. "A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance," Energy, Elsevier, vol. 277(C).
    16. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    17. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    18. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    19. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    20. Ansari, Amir Babak & Esfahanian, Vahid & Torabi, Farschad, 2016. "Discharge, rest and charge simulation of lead-acid batteries using an efficient reduced order model based on proper orthogonal decomposition," Applied Energy, Elsevier, vol. 173(C), pages 152-167.

    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:14:y:2021:i:15:p:4644-:d:605733. 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.