Journal
JOURNAL OF POWER SOURCES
Volume 335, Issue -, Pages 121-130Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jpowsour.2016.10.040
Keywords
Multi-time-scale observer design; State-of-charge; State-of-health; Lithium-ion battery state estimation; Electrochemical model; Model reduction
Funding
- National Information Communication Technology, Australia (NICTA)
- Australian Research Council [FT100100538]
- Australian Research Council [FT100100538] Funding Source: Australian Research Council
Ask authors/readers for more resources
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-time scale 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. (C) 2016 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available