Nonlinear Filtering Techniques Comparison for Battery State Estimation

Aspasia Papazoglou, Stefano Longo, Daniel Auger, Francis Assadian
2014 Journal of Sustainable Development of Energy, Water and Environment Systems  
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
more » ... 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.
doi:10.13044/j.sdewes.2014.02.0021 fatcat:g7v73k2habcdhhfddrpxuuebki