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Online estimation of satellite lithium-ion battery capacity based on approximate belief rule base and hidden Markov model

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  • Zhao, Dao
  • Zhou, Zhijie
  • Tang, Shuaiwen
  • Cao, You
  • Wang, Jie
  • Zhang, Peng
  • Zhang, Yijun

Abstract

To ensure safety of satellite operation in orbit, it is important to estimate the capacity of lithium-ion battery in time. However, the battery capacity cannot be measured directly in space, and it is continually changing due to the usage that changes with the space environment. Because of the complex electrochemical side reactions inside battery, it is difficult to establish an accurate model. To solve the above problems, eight features from battery usage of three processes are selected to comprehensively depict the change of battery capacity. Taking the battery capacity as hidden state, an online estimation model is proposed based on approximate belief rule base and hidden Markov model by using historical data and expert knowledge. Based on the performance test data of a certain type of satellite battery, the effectiveness of the proposed model is verified. The capacity of an in-orbit satellite battery is estimated and analyzed by using the telemetry data.

Suggested Citation

  • Zhao, Dao & Zhou, Zhijie & Tang, Shuaiwen & Cao, You & Wang, Jie & Zhang, Peng & Zhang, Yijun, 2022. "Online estimation of satellite lithium-ion battery capacity based on approximate belief rule base and hidden Markov model," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222015353
    DOI: 10.1016/j.energy.2022.124632
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Michael W. Hopwood & Lekha Patel & Thushara Gunda, 2022. "Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach," Energies, MDPI, vol. 15(14), pages 1-12, July.
    3. Diego Castanho & Marcio Guerreiro & Ludmila Silva & Jony Eckert & Thiago Antonini Alves & Yara de Souza Tadano & Sergio Luiz Stevan & Hugo Valadares Siqueira & Fernanda Cristina Corrêa, 2022. "Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization," Energies, MDPI, vol. 15(19), pages 1-21, September.
    4. Wang, Shuhui & Wang, Zhenpo & Cheng, Ximing & Zhang, Zhaosheng, 2023. "A double-layer fault diagnosis strategy for electric vehicle batteries based on Gaussian mixture model," Energy, Elsevier, vol. 281(C).

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