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Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer

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  • Sun, Li
  • Li, Guanru
  • You, Fengqi

Abstract

Lithium-ion battery is considered as one of the most successful energy storage methods which enables the sustainability of the renewable energy systems subject to high intermittency. To avoid the permanent damage and the potential explosion, the battery state-of-charge (SOC) serves as a characteristic operational parameter that should be maintained within a safe range. However, accurate SOC estimation is challenging in the presence of uncertainties, particularly due to the problems of unknown initial SOC value and the uncertain internal resistance. To address this uncertainties, this paper critically reviews the state-of-the-art of the current SOC researches and takes actions to propose a joint SOC and internal resistance estimation algorithm in a real-time data-driven manner. Both static and dynamic experiments are carried out to identify an equivalent circuit model (ECM) of the battery dynamics. The semi-empirical model's parameters are optimized using the experimental data. Sensitivity analysis is then carried out to reveal that the internal resistance is the dominating parameter that affects the model accuracy. A conventional model-based nonlinear state observer is developed to accommodate the initial value uncertainty. To deal with the uncertain internal resistance through this state observer, internal resistance is treated as an augmented state, which is estimated together with SOC based on extended state observer (ESO). The conclusions are drawn from the experimental results in two aspects, i) a 83% performance improvement of the proposed ESO method is achieved when compared with the conventional observer without internal resistance augmentation; ii) the dynamic variation of the internal resistance is captured by the proposed method, which is shown to increase with the service time. The proposed method is able to give a simultaneous estimation of SOC and internal resistance, depicting a promising prospect in the future commercial application.

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  • 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).
    • Handle: RePEc:eee:rensus:v:131:y:2020:i:c:s1364032120302859
    DOI: 10.1016/j.rser.2020.109994
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    as
    1. Waag, Wladislaw & Sauer, Dirk Uwe, 2013. "Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination," Applied Energy, Elsevier, vol. 111(C), pages 416-427.
    2. Astudillo, Miguel F. & Vaillancourt, Kathleen & Pineau, Pierre-Olivier & Amor, Ben, 2017. "Can the household sector reduce global warming mitigation costs? sensitivity to key parameters in a TIMES techno-economic energy model," Applied Energy, Elsevier, vol. 205(C), pages 486-498.
    3. Sun, Li & Jin, Yuhui & You, Fengqi, 2020. "Active disturbance rejection temperature control of open-cathode proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 261(C).
    4. Zubi, Ghassan & Dufo-López, Rodolfo & Carvalho, Monica & Pasaoglu, Guzay, 2018. "The lithium-ion battery: State of the art and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 292-308.
    5. 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.
    6. Carl-Friedrich Schleussner & Claire L. Fyson, 2020. "Scenarios science needed in UNFCCC periodic review," Nature Climate Change, Nature, vol. 10(4), pages 272-272, April.
    7. Jaiswal, Abhishek, 2017. "Lithium-ion battery based renewable energy solution for off-grid electricity: A techno-economic analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 922-934.
    8. Tervo, Eric & Agbim, Kenechi & DeAngelis, Freddy & Hernandez, Jeffrey & Kim, Hye Kyung & Odukomaiya, Adewale, 2018. "An economic analysis of residential photovoltaic systems with lithium ion battery storage in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1057-1066.
    9. Bizhong Xia & Shengkun Guo & Wei Wang & Yongzhi Lai & Huawen Wang & Mingwang Wang & Weiwei Zheng, 2018. "A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries," Energies, MDPI, vol. 11(10), pages 1-15, October.
    10. Huang, Deyang & Chen, Ziqiang & Zheng, Changwen & Li, Haibin, 2019. "A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature," Energy, Elsevier, vol. 185(C), pages 847-861.
    11. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    12. Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    13. 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).
    14. Sturm, J. & Ennifar, H. & Erhard, S.V. & Rheinfeld, A. & Kosch, S. & Jossen, A., 2018. "State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter," Applied Energy, Elsevier, vol. 223(C), pages 103-123.
    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. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    17. Ruiz, V. & Pfrang, A. & Kriston, A. & Omar, N. & Van den Bossche, P. & Boon-Brett, L., 2018. "A review of international abuse testing standards and regulations for lithium ion batteries in electric and hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1427-1452.
    18. Sun, Li & Li, Guanru & Hua, Q.S. & Jin, Yuhui, 2020. "A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control," Renewable Energy, Elsevier, vol. 147(P1), pages 1642-1652.
    19. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    20. Pastor-Fernández, Carlos & Yu, Tung Fai & Widanage, W. Dhammika & Marco, James, 2019. "Critical review of non-invasive diagnosis techniques for quantification of degradation modes in lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 138-159.
    21. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    22. Sun, Li & Shen, Jiong & Hua, Qingsong & Lee, Kwang Y., 2018. "Data-driven oxygen excess ratio control for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 231(C), pages 866-875.
    23. M. S. Dresselhaus & I. L. Thomas, 2001. "Alternative energy technologies," Nature, Nature, vol. 414(6861), pages 332-337, November.
    24. 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.
    Full references (including those not matched with items on IDEAS)

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