IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v150y2018icp759-769.html
   My bibliography  Save this article

Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries

Author

Listed:
  • Zheng, Linfeng
  • Zhu, Jianguo
  • Lu, Dylan Dah-Chuan
  • Wang, Guoxiu
  • He, Tingting

Abstract

The reliability and safety of battery operations necessitate an efficient battery management system (BMS) with accurate battery state of charge (SOC) and capacity estimation techniques. This paper investigates the incremental capacity analysis (ICA) and differential voltage analysis (DVA) methods for onboard battery SOC and capacity estimation. Since the conventional cell terminal voltage based ICA/DVA methods are sensitive to the changed battery resistance and polarization during battery aging processes, the SOC based ICA/DVA methods are proposed to address this problem as so to accurately identify features of interest on incremental capacity (IC) and differential voltage (DV) curves for applications. Three feature points (FPs) that are potential to be easily identified by battery management systems are extracted from the SOC based IC/DV curves, and then the relations between FPs and cell SOCs/capacities are quantified and applied for battery SOC and capacity estimation. The robustness of the proposed approach against various aging levels and erroneous cumulative capacities is evaluated. Promising results with the maximum absolute error of 1.0% and the relative error of 2.0% can be achieved for battery SOC and capacity estimation, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:150:y:2018:i:c:p:759-769
    DOI: 10.1016/j.energy.2018.03.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218304213
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.03.023?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. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & He, Tingting & Wei, Yiying, 2016. "Novel methods for estimating lithium-ion battery state of energy and maximum available energy," Applied Energy, Elsevier, vol. 178(C), pages 1-8.
    2. Zheng, Linfeng & Zhang, Lei & Zhu, Jianguo & Wang, Guoxiu & Jiang, Jiuchun, 2016. "Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model," Applied Energy, Elsevier, vol. 180(C), pages 424-434.
    3. He, Yao & Liu, XingTao & Zhang, ChenBin & Chen, ZongHai, 2013. "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, Elsevier, vol. 101(C), pages 808-814.
    4. Pan, Haihong & Lü, Zhiqiang & Lin, Weilong & Li, Junzi & Chen, Lin, 2017. "State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model," Energy, Elsevier, vol. 138(C), pages 764-775.
    5. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    6. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    7. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    8. Hu, Xiaosong & Li, Shengbo Eben & Jia, Zhenzhong & Egardt, Bo, 2014. "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, Elsevier, vol. 64(C), pages 953-960.
    9. Li, Xue & Jiang, Jiuchun & Wang, Le Yi & Chen, Dafen & Zhang, Yanru & Zhang, Caiping, 2016. "A capacity model based on charging process for state of health estimation of lithium ion batteries," Applied Energy, Elsevier, vol. 177(C), pages 537-543.
    10. Wei, Zhongbao & Meng, Shujuan & Xiong, Binyu & Ji, Dongxu & Tseng, King Jet, 2016. "Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer," Applied Energy, Elsevier, vol. 181(C), pages 332-341.
    11. Berrueta, Alberto & Urtasun, Andoni & Ursúa, Alfredo & Sanchis, Pablo, 2018. "A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model," Energy, Elsevier, vol. 144(C), pages 286-300.
    12. Ye, Min & Guo, Hui & Xiong, Rui & Yu, Quanqing, 2018. "A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries," Energy, Elsevier, vol. 144(C), pages 789-799.
    13. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    14. 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.
    15. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    16. Lu, Chen & Zhang, Lipin & Ma, Jian & Chen, Zihan & Tao, Laifa & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2017. "Li-ion battery capacity cycling fading dynamics cognition: A stochastic approach," Energy, Elsevier, vol. 137(C), pages 251-259.
    17. Bai, Guangxing & Wang, Pingfeng & Hu, Chao & Pecht, Michael, 2014. "A generic model-free approach for lithium-ion battery health management," Applied Energy, Elsevier, vol. 135(C), pages 247-260.
    18. Wu, Zhou & Ling, Rui & Tang, Ruoli, 2017. "Dynamic battery equalization with energy and time efficiency for electric vehicles," Energy, Elsevier, vol. 141(C), pages 937-948.
    19. Uddin, Kotub & Jackson, Tim & Widanage, Widanalage D. & Chouchelamane, Gael & Jennings, Paul A. & Marco, James, 2017. "On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system," Energy, Elsevier, vol. 133(C), pages 710-722.
    20. Berecibar, Maitane & Garmendia, Maitane & Gandiaga, Iñigo & Crego, Jon & Villarreal, Igor, 2016. "State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application," Energy, Elsevier, vol. 103(C), pages 784-796.
    21. Chen, Kunlong & Zheng, Fangdan & Jiang, Jiuchun & Zhang, Weige & Jiang, Yan & Chen, Kunjin, 2017. "Practical failure recognition model of lithium-ion batteries based on partial charging process," Energy, Elsevier, vol. 138(C), pages 1199-1208.
    22. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    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. 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).
    2. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    3. 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.
    4. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2017. "Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes," Energy, Elsevier, vol. 135(C), pages 257-268.
    5. 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.
    6. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe & Xu, Tianjiao, 2019. "Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. 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.
    8. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    9. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    10. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    11. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
    12. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2018. "Successive-approximation algorithm for estimating capacity of Li-ion batteries," Energy, Elsevier, vol. 159(C), pages 61-73.
    13. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    14. Wei, Jingwen & Dong, Guangzhong & Chen, Zonghai & Kang, Yu, 2017. "System state estimation and optimal energy control framework for multicell lithium-ion battery system," Applied Energy, Elsevier, vol. 187(C), pages 37-49.
    15. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    16. 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.
    17. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    18. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2017. "A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique," Energy, Elsevier, vol. 141(C), pages 1402-1415.
    19. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    20. Yujie Cheng & Laifa Tao & Chao Yang, 2017. "Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition," Complexity, Hindawi, vol. 2017, pages 1-13, December.

    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:energy:v:150:y:2018:i:c:p:759-769. 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.journals.elsevier.com/energy .

    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.