LMI-based robust observer design for battery state-of-charge estimation

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Estimating the battery State-of-Charge (SoC) is often done using nonlinear extensions of the Kalman filter. These filters do not explicitly address convergence of the estimation error and robustness with respect to model uncertainty, and make nonrealistic assumptions on the noise. Therefore, these filters require extensive tuning of the covariance matrices, which is a non-intuitive and tedious task. In this paper, a robust Luenberger estimator is proposed that explicitly addresses the requirements on estimation-error convergence, robustness and noise attenuation and shows their inherent trade-off. Different observers are synthesised using polytopic embeddings of the nonlinear battery model and using linear matrix inequalities that provide bounds on the {ell-{2,infty}-, ell-{infty,infty}-} or the ell-{2,2}-gains between input and output (to accommodate for model uncertainty and sensor noise). This guarantees a robustly converging SoC observer and makes its design more intuitive. The proposed observers are validated and compared with an Extended Kalman Filter (EKF) using experimental data. The results show that the performance of two out of three proposed observers is similar to the EKF, while the implementation is simpler and tuning is more intuitive and more straightforward.

Originele taal-2Engels
Titel2018 IEEE Conference on Decision and Control, CDC 2018
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's5716-5721
Aantal pagina's6
ISBN van elektronische versie9781538613955
DOI's
StatusGepubliceerd - 2018
Evenement57th IEEE Conference on Decision and Control, (CDC2018) - Miami, Verenigde Staten van Amerika
Duur: 17 dec 201819 dec 2018
Congresnummer: 57

Congres

Congres57th IEEE Conference on Decision and Control, (CDC2018)
Verkorte titelCDC 2018
LandVerenigde Staten van Amerika
StadMiami
Periode17/12/1819/12/18

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