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Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures

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  • Seo, Minhwan
  • Song, Youngbin
  • Kim, Jake
  • Paek, Sung Wook
  • Kim, Gi-Heon
  • Kim, Sang Woo

Abstract

Ambient temperature alters properties of lithium-ion batteries and affects the accuracy of estimation of state of charge (SOC), which is an important function to ensure the safety and reliability of electric vehicles. An accurate SOC estimation is critical under various temperatures. The existing methods have two problems: 1) need to perform time-consuming pre-experiments to investigate influence of various temperatures on the battery properties, and 2) use of the Thevenin model which is inaccurate at sub-zero temperatures. This study proposes an innovative lumped-battery model to improve the accuracy of both SOC estimation and battery modeling without pre-experiments. Two main causes of modeling errors by the Thevenin model are analyzed. Proposed model parameters are estimated using the recursive least squares, and the extended Kalman filter is used to estimate SOC in real time. Experiments are conducted under time-invariant and time-varying temperature conditions ranging from −10 °C to 30 °C. The results indicate that relative errors of battery modeling are less than 2.4% and that estimation errors of SOC are at most 0.4% under various temperatures. Therefore, the proposed method can be conveniently and widely applied to all-climate battery management systems to achieve a high accuracy of SOC estimation.

Suggested Citation

  • Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221005508
    DOI: 10.1016/j.energy.2021.120301
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    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. Dian Wang & Yun Bao & Jianjun Shi, 2017. "Online Lithium-Ion Battery Internal Resistance Measurement Application in State-of-Charge Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-11, August.
    3. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    4. Wang, Yujie & Liu, Chang & Pan, Rui & Chen, Zonghai, 2017. "Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator," Energy, Elsevier, vol. 121(C), pages 739-750.
    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. Zhang, Yu & Peng, Zhen & Guan, Yong & Wu, Lifeng, 2021. "Prognostics of battery cycle life in the early-cycle stage based on hybrid model," Energy, Elsevier, vol. 221(C).
    7. Bi, Jun & Wang, Yongxing & Sai, Qiuyue & Ding, Cong, 2019. "Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China," Energy, Elsevier, vol. 169(C), pages 833-843.
    8. Zheng, Yuejiu & Qin, Chao & Lai, Xin & Han, Xuebing & Xie, Yi, 2019. "A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. 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.
    10. 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.
    11. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    12. Li, Jianwei & Gee, Anthony M. & Zhang, Min & Yuan, Weijia, 2015. "Analysis of battery lifetime extension in a SMES-battery hybrid energy storage system using a novel battery lifetime model," Energy, Elsevier, vol. 86(C), pages 175-185.
    13. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    14. 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.
    15. Zheng, Fangdan & Xing, Yinjiao & Jiang, Jiuchun & Sun, Bingxiang & Kim, Jonghoon & Pecht, Michael, 2016. "Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 183(C), pages 513-525.
    16. Shuqing Li & Chuankun Ju & Jianliang Li & Ri Fang & Zhifei Tao & Bo Li & Tingting Zhang, 2021. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network," Energies, MDPI, vol. 14(2), pages 1-21, January.
    17. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
    18. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
    19. 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.
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    7. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
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