State-of-Charge Estimation for Batteries: A Multi-model Approach

    •  Fang, H., Zhao, X., Wang, Y., Sahinoglu, Z., Wada, T., Hara, S., de Callafon, R.A., "State-of-Charge Estimation for Batteries: A Multi-model Approach", American Control Conference (ACC), DOI: 10.1109/ACC.2014.6858976, June 2014, pp. 2779-2785.
      BibTeX TR2014-044 PDF
      • @inproceedings{Fang2014jun,
      • author = {Fang, H. and Zhao, X. and Wang, Y. and Sahinoglu, Z. and Wada, T. and Hara, S. and {de Callafon}, R.A.},
      • title = {State-of-Charge Estimation for Batteries: A Multi-model Approach},
      • booktitle = {American Control Conference (ACC)},
      • year = 2014,
      • pages = {2779--2785},
      • month = jun,
      • publisher = {IEEE},
      • doi = {10.1109/ACC.2014.6858976},
      • issn = {0743-1619},
      • isbn = {978-1-4799-3272-6},
      • url = {}
      • }
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  • Research Areas:

    Dynamical Systems, Electric Systems, Signal Processing

Monitoring the state-of-charge (SoC) for batteries is challenging, especially when a battery has time-varying parameters. We propose to improve SoC estimation using an adaptive strategy and multiple models in this study, developing a unique algorithm called MM-AdaSoC. Specifically, two submodels in state-space form are generated from a modified Nernst battery model. Both are shown to be locally observable under mild conditions. The iterated extended Kalman filter (IEKF) is then applied to each submodel in parallel, estimating simultaneously the SoC variable and certain unknown parameters. The SoC estimates obtained from the two separately implemented IEKFs are fused to yield the final overall SoC estimates, which tend to have higher accuracy than those obtained from a single-model. Its effectiveness is demonstrated via experiments.