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State estimation for advanced battery management: Key challenges and future trends

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Listed:
  • Hu, Xiaosong
  • Feng, Fei
  • Liu, Kailong
  • Zhang, Lei
  • Xie, Jiale
  • Liu, Bo

Abstract

Batteries are presently pervasive in portable electronics, electrified vehicles, and renewable energy storage. These indispensable engineering applications are all safety-critical and energy efficiency-demanding such that batteries must be meticulously monitored and manipulated, where effectively estimating the internal battery states is a key enabler. The primary goal of this paper is to present a concise, understandable overview of existing methods, key issues, technical challenges, and future trends of the battery state estimation domain. More specifically, for the first time, the state of the art in State of Charge (SOC), State of Energy (SOE), State of Health (SOH), State of Power (SOP), State of Temperature (SOT), and State of Safety (SOS) estimation is all elucidated in a tutorial yet systematical way, along with existing issues exposed. In addition, from six different viewpoints, some future important research opportunities and evolving trends of this prosperous field are disclosed, in order to stimulate more technologically innovative breakthroughs in SOC/SOE/SOH/SOP/SOT/SOS estimation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:114:y:2019:i:c:32
    DOI: 10.1016/j.rser.2019.109334
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    as
    1. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.
    2. Zhang, Xu & Wang, Yujie & Wu, Ji & Chen, Zonghai, 2018. "A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter," Applied Energy, Elsevier, vol. 216(C), pages 442-451.
    3. 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.
    4. Feng, Fei & Hu, Xiaosong & Hu, Lin & Hu, Fengling & Li, Yang & Zhang, Lei, 2019. "Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 102-113.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    10. Jiale Xie & Jiachen Ma & Jun Chen, 2018. "Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect," Energies, MDPI, vol. 11(5), pages 1-14, May.
    11. Dong, Guangzhong & Zhang, Xu & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state of energy estimation of lithium-ion batteries based on neural network model," Energy, Elsevier, vol. 90(P1), pages 879-888.
    12. Wang, Qian & Jiang, Bin & Li, Bo & Yan, Yuying, 2016. "A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 106-128.
    13. Lin, Cheng & Mu, Hao & Xiong, Rui & Cao, Jiayi, 2017. "Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy," Applied Energy, Elsevier, vol. 194(C), pages 560-568.
    14. 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.
    15. Burgos-Mellado, Claudio & Orchard, Marcos E. & Kazerani, Mehrdad & Cárdenas, Roberto & Sáez, Doris, 2016. "Particle-filtering-based estimation of maximum available power state in Lithium-Ion batteries," Applied Energy, Elsevier, vol. 161(C), pages 349-363.
    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. Pei, Lei & Zhu, Chunbo & Wang, Tiansi & Lu, Rengui & Chan, C.C., 2014. "Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 66(C), pages 766-778.
    18. Song, Ziyou & Hofmann, Heath & Lin, Xinfan & Han, Xuebing & Hou, Jun, 2018. "Parameter identification of lithium-ion battery pack for different applications based on Cramer-Rao bound analysis and experimental study," Applied Energy, Elsevier, vol. 231(C), pages 1307-1318.
    19. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
    20. Ming-Hui Chang & Han-Pang Huang & Shu-Wei Chang, 2013. "A New State of Charge Estimation Method for LiFePO 4 Battery Packs Used in Robots," Energies, MDPI, vol. 6(4), pages 1-24, April.
    21. 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.
    22. Jun Lu & Tianpin Wu & Khalil Amine, 2017. "State-of-the-art characterization techniques for advanced lithium-ion batteries," Nature Energy, Nature, vol. 2(3), pages 1-13, March.
    23. 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.
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