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A review of stochastic battery models and health management

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  • Tao, Laifa
  • Ma, Jian
  • Cheng, Yujie
  • Noktehdan, Azadeh
  • Chong, Jin
  • Lu, Chen

Abstract

Batteries are promising sources of green and sustainable energy that have been widely used in various applications. Battery modelling as the basis of battery management system is vital for both technology development and applications of batteries. Compared with other battery models, stochastic battery models feature high accuracy and low time consumption. Moreover, charging profile, battery behavior, and discharging profile can all be considered to optimize battery performance and usage, which is a key issue in battery usage in real life. Given the significance of stochastic modelling and the progress of battery health management, this paper reviews various aspects of related studies and developments from different fields, while identifying their corresponding merits and weaknesses. Remaining challenges are discussed, and several suggestions are offered as possible inspirations for further research.

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  • Tao, Laifa & Ma, Jian & Cheng, Yujie & Noktehdan, Azadeh & Chong, Jin & Lu, Chen, 2017. "A review of stochastic battery models and health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 716-732.
    • Handle: RePEc:eee:rensus:v:80:y:2017:i:c:p:716-732
    DOI: 10.1016/j.rser.2017.05.127
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    3. Jinsong Yu & Baohua Mo & Diyin Tang & Jie Yang & Jiuqing Wan & Jingjing Liu, 2017. "Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use," Energies, MDPI, vol. 10(12), pages 1-19, December.
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