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Robust state of charge and state of health estimation for batteries using a novel multi model approach

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Abstract

Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system (BMS). Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system. We propose a new robust method, called ERMES (extendible range multi-model estimator), for determining an estimated state-of-charge (SoC), an estimated state-of-health (SoH) and a prediction of uncertainty of the estimates (state-of-uncertainty—SoU), thanks to which it is possible to monitor the validity of the estimates and adjust it, extending the robustness against a wider range of uncertainty, if necessary. Specifically, a finite number of models in state-space form are considered starting from a modified Thevenin battery model. Each model is characterized by a hypothesis of SoH value. An iterated extended Kalman filter (EKF) is then applied to each model in parallel, estimating for each one the SoC state variable. Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF, yielding the overall SoC and SoH estimates, respectively. In addition, a figure of uncertainty of such estimates is also provided.

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Acknowledgements

Prof. Eros Pasero, DET department, director of Neuronic Laboratory of polytechnic School of engineering of Turin, for huge support about testing procedures and HW design. Prof. Alessandro Rizzo, DAUIN department of Polytechnic School of Engineering of Turin, for methodological support and advices profused in development of this work. Innovation Cluster Mechatronics of Regione Piemonte, MESAP and Regione Piemonte for supporting this work.

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  1. Davide Faverato, Marco Colabella and Gianluca Buonomo contributed equally to this work.

Authors and Affiliations

  1. Innovation Department, Brain Technologies, Corso Tazzoli 215/12B, Turin, 10137, Italy

    Giovanni Guida, Davide Faverato, Marco Colabella & Gianluca Buonomo

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  1. Giovanni Guida
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Correspondence to Giovanni Guida.

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Guida, G., Faverato, D., Colabella, M. et al. Robust state of charge and state of health estimation for batteries using a novel multi model approach. Control Theory Technol. 20, 418–438 (2022). https://doi.org/10.1007/s11768-022-00103-0

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