Original scientific paper
https://doi.org/10.13044/j.sdewes.2014.02.0021
Nonlinear Filtering Techniques Comparison for Battery State Estimation
Aspasia Papazoglou
; Centre for Automotive Engineering, Cranfield University, Bedfordshire, UK
Stefano Longo
; Centre for Automotive Engineering, Cranfield University, Bedfordshire, UK
Daniel Auger
; Centre for Automotive Engineering, Cranfield University, Bedfordshire, UK
Francis Assadian
; Centre for Automotive Engineering, Cranfield University, Bedfordshire, UK
Abstract
The performance of estimation algorithms is vital for the correct functioning of batteries in electric vehicles, as poor estimates will inevitably jeopardize the operations that rely on un-measurable quantities, such as State of Charge and State of Health. This paper compares the performance of three nonlinear estimation algorithms: the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter, where a lithium-ion cell model is considered. The effectiveness of these algorithms is measured by their ability to produce accurate estimates against their computational complexity in terms of number of operations and execution time required. The trade-offs between estimators' performance and their computational complexity are analyzed.
Keywords
Battery-management system; Estimation algorithms; Lithium-ion cells; State of Health; Computational complexity
Hrčak ID:
126192
URI
Publication date:
30.9.2014.
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