IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v204y2017icp1240-1250.html
   My bibliography  Save this article

Non-destructive fast charging algorithm of lithium-ion batteries based on the control-oriented electrochemical model

Author

Listed:
  • Chu, Zhengyu
  • Feng, Xuning
  • Lu, Languang
  • Li, Jianqiu
  • Han, Xuebing
  • Ouyang, Minggao

Abstract

Fast charging is critical for the application of lithium-ion batteries in electric vehicles. Conventional fast charging algorithms may shorten the cycle life of lithium-ion batteries and induce safety problems, such as internal short circuit caused by lithium deposition at the negative electrode. In this paper, a novel, non-destructive model-based fast charging algorithm is proposed. The fast charging algorithm is composed of two closed loops. The first loop includes an anode over-potential observer that can observe the status of lithium deposition online, whereas the second loop includes a feedback structure that can modify the current based on the observed status of lithium deposition. The charging algorithm enhances the charging current to maintain the observed anode over-potential near the preset threshold potential. Therefore, the fast charging algorithm can decrease the charging time while protecting the health of the battery. The fast charging algorithm is validated on a commercial large-format nickel cobalt manganese/graphite cell. The results showed that 96.8% of the battery capacity can be charged within 52min. The post-mortem observation of the surface of the negative electrode and degradation tests revealed that the fast charging algorithm proposed here protected the battery from lithium deposition.

Suggested Citation

  • Chu, Zhengyu & Feng, Xuning & Lu, Languang & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2017. "Non-destructive fast charging algorithm of lithium-ion batteries based on the control-oriented electrochemical model," Applied Energy, Elsevier, vol. 204(C), pages 1240-1250.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:1240-1250
    DOI: 10.1016/j.apenergy.2017.03.111
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917303501
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.03.111?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sun, Fengchun & Xiong, Rui & He, Hongwen & Li, Weiqing & Aussems, Johan Eric Emmanuel, 2012. "Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries," Applied Energy, Elsevier, vol. 96(C), pages 378-386.
    2. Feng, Xuning & Weng, Caihao & Ouyang, Minggao & Sun, Jing, 2016. "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Elsevier, vol. 161(C), pages 168-180.
    3. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    4. Ouyang, Minggao & Feng, Xuning & Han, Xuebing & Lu, Languang & Li, Zhe & He, Xiangming, 2016. "A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery," Applied Energy, Elsevier, vol. 165(C), pages 48-59.
    5. Polterovich, Victor & Popov, Vladimir, 2006. "Эволюционная Теория Экономической Политики: Часть I: Опыт Быстрого Развития [An Evolutionary Theory of Economic Policy: Part I: The Experience of Fast Development]," MPRA Paper 22168, University Library of Munich, Germany.
    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. Angeles Cabañero, Maria & Altmann, Johannes & Gold, Lukas & Boaretto, Nicola & Müller, Jana & Hein, Simon & Zausch, Jochen & Kallo, Josef & Latz, Arnulf, 2019. "Investigation of the temperature dependence of lithium plating onset conditions in commercial Li-ion batteries," Energy, Elsevier, vol. 171(C), pages 1217-1228.
    2. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    3. Li, Junqiu & Sun, Danni & Jin, Xin & Shi, Wentong & Sun, Chao, 2019. "Lithium-ion battery overcharging thermal characteristics analysis and an impedance-based electro-thermal coupled model simulation," Applied Energy, Elsevier, vol. 254(C).
    4. Zou, Changfu & Hu, Xiaosong & Wei, Zhongbao & Tang, Xiaolin, 2017. "Electrothermal dynamics-conscious lithium-ion battery cell-level charging management via state-monitored predictive control," Energy, Elsevier, vol. 141(C), pages 250-259.
    5. Hong Zhao & Li Wang & Zonghai Chen & Xiangming He, 2019. "Challenges of Fast Charging for Electric Vehicles and the Role of Red Phosphorous as Anode Material: Review," Energies, MDPI, vol. 12(20), pages 1-23, October.
    6. Yang, Xiaofeng & He, Hongwen & Wei, Zhongbao & Wang, Rui & Xu, Ke & Zhang, Dong, 2023. "Enabling Safety-Enhanced fast charging of electric vehicles via soft actor Critic-Lagrange DRL algorithm in a Cyber-Physical system," Applied Energy, Elsevier, vol. 329(C).
    7. Lai, Xin & Huang, Yunfeng & Deng, Cong & Gu, Huanghui & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2021. "Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    8. Zhuangzhuang Cui & Zhuangzhuang Jia & Digen Ruan & Qingshun Nian & Jiajia Fan & Shunqiang Chen & Zixu He & Dazhuang Wang & Jinyu Jiang & Jun Ma & Xing Ou & Shuhong Jiao & Qingsong Wang & Xiaodi Ren, 2024. "Molecular anchoring of free solvents for high-voltage and high-safety lithium metal batteries," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Jiang, Benben & Berliner, Marc D. & Lai, Kun & Asinger, Patrick A. & Zhao, Hongbo & Herring, Patrick K. & Bazant, Martin Z. & Braatz, Richard D., 2022. "Fast charging design for Lithium-ion batteries via Bayesian optimization," Applied Energy, Elsevier, vol. 307(C).
    10. Gao, Yizhao & Liu, Chenghao & Chen, Shun & Zhang, Xi & Fan, Guodong & Zhu, Chong, 2022. "Development and parameterization of a control-oriented electrochemical model of lithium-ion batteries for battery-management-systems applications," Applied Energy, Elsevier, vol. 309(C).
    11. Sattarzadeh, Sara & Padisala, Shanthan K. & Shi, Ying & Mishra, Partha Pratim & Smith, Kandler & Dey, Satadru, 2023. "Feedback-based fault-tolerant and health-adaptive optimal charging of batteries," Applied Energy, Elsevier, vol. 343(C).
    12. Lin, Qian & Wang, Jun & Xiong, Rui & Shen, Weixiang & He, Hongwen, 2019. "Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries," Energy, Elsevier, vol. 183(C), pages 220-234.

    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. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    2. Dai, Haifeng & Xu, Tianjiao & Zhu, Letao & Wei, Xuezhe & Sun, Zechang, 2016. "Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales," Applied Energy, Elsevier, vol. 184(C), pages 119-131.
    3. Hu, Xiaosong & Jiang, Haifu & Feng, Fei & Liu, Bo, 2020. "An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management," Applied Energy, Elsevier, vol. 257(C).
    4. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    5. Quanqing Yu & Changjiang Wan & Junfu Li & Rui Xiong & Zeyu Chen, 2021. "A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles," Energies, MDPI, vol. 14(4), pages 1-15, February.
    6. Ren, Dongsheng & Liu, Xiang & Feng, Xuning & Lu, Languang & Ouyang, Minggao & Li, Jianqiu & He, Xiangming, 2018. "Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components," Applied Energy, Elsevier, vol. 228(C), pages 633-644.
    7. Weng, Caihao & Feng, Xuning & Sun, Jing & Peng, Huei, 2016. "State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking," Applied Energy, Elsevier, vol. 180(C), pages 360-368.
    8. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    9. Son, Seho & Jeong, Siheon & Kwak, Eunji & Kim, Jun-hyeong & Oh, Ki-Yong, 2022. "Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features," Energy, Elsevier, vol. 238(PA).
    10. Qiao, Dongdong & Wang, Xueyuan & Lai, Xin & Zheng, Yuejiu & Wei, Xuezhe & Dai, Haifeng, 2022. "Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method," Energy, Elsevier, vol. 243(C).
    11. Saw, L.H. & Ye, Y. & Tay, A.A.O., 2014. "Electro-thermal analysis and integration issues of lithium ion battery for electric vehicles," Applied Energy, Elsevier, vol. 131(C), pages 97-107.
    12. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    13. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
    14. Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhou, Long & Zhang, Zhendong, 2016. "Recording frequency optimization for massive battery data storage in battery management systems," Applied Energy, Elsevier, vol. 183(C), pages 380-389.
    15. Li, Shi & Pischinger, Stefan & He, Chaoyi & Liang, Liliuyuan & Stapelbroek, Michael, 2018. "A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test," Applied Energy, Elsevier, vol. 212(C), pages 1522-1536.
    16. Liu, Xinyang & Zheng, Zhuoyuan & Büyüktahtakın, İ. Esra & Zhou, Zhi & Wang, Pingfeng, 2021. "Battery asset management with cycle life prognosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    17. Ouyang, Minggao & Gao, Shang & Lu, Languang & Feng, Xuning & Ren, Dongsheng & Li, Jianqiu & Zheng, Yuejiu & Shen, Ping, 2016. "Determination of the battery pack capacity considering the estimation error using a Capacity–Quantity diagram," Applied Energy, Elsevier, vol. 177(C), pages 384-392.
    18. Cuma, Mehmet Ugras & Koroglu, Tahsin, 2015. "A comprehensive review on estimation strategies used in hybrid and battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 517-531.
    19. Taimoor Zahid & Weimin Li, 2016. "A Comparative Study Based on the Least Square Parameter Identification Method for State of Charge Estimation of a LiFePO 4 Battery Pack Using Three Model-Based Algorithms for Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-16, September.
    20. Li, Shen & Marzook, Mohamed Waseem & Zhang, Cheng & Offer, Gregory J. & Marinescu, Monica, 2023. "How to enable large format 4680 cylindrical lithium-ion batteries," Applied Energy, Elsevier, vol. 349(C).

    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:eee:appene:v:204:y:2017:i:c:p:1240-1250. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.