Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery
Artikel i vetenskaplig tidskrift, 2016

The accurate online state estimation for some types of nonlinear singularly perturbed systems is challenging due to extensive computational requirements, ill-conditioned gains and/or convergence issues. This paper proposes a multi-time-scale estimation algorithm for a class of nonlinear systems with coupled fast and slow dynamics. Based on a boundary-layer model and a reduced model, a multi-timescale estimator is proposed in which the design parameter sets can be tuned in different time-scales. Stability property of the estimation errors is analytically characterized by adopting a deterministic version of extended Kalman filter (EKF). This proposed algorithm is applied to estimator design for the state-of-charge (SOC) and state-of-health (SOH) in a lithium-ion battery using the developed reduced order battery models. Simulation results on a high fidelity lithium-ion battery model demonstrate that the observer is effective in estimating SOC and SOH despite a range of common errors due to model order reductions, linearisation, initialisation and noisy measurement.



Multi-time-scale observer design

Lithium-ion battery


Changfu Zou

University of Melbourne

Chris Manzie

University of Melbourne

Dragan Nesic

University of Melbourne

Abhijit Kallapur

University of Melbourne

Journal of Power Sources

0378-7753 (ISSN)

Vol. 335 121-130


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