Under the patronage of:

Journal Metrics


Impact factor (2022): 2.1

5.4
2023CiteScore
 
75th percentile
Powered by  Scopus


More about CiteScore


Source Normalized Impact per Paper (SNIP): 0.474


SCImago Journal Rank (SJR): 0.377

 
 

Nonlinear Filtering Techniques Comparison for Battery State Estimation

Original scientific paper

Journal of Sustainable Development of Energy, Water and Environment Systems
Volume 2, Issue 3, September 2014, pp 259-269
DOI: https://doi.org/10.13044/j.sdewes.2014.02.0021
Aspasia Papazoglou, Stefano Longo , Daniel Auger, 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

Creative Commons License
Views (in 2024): 315 | Downloads (in 2024): 107
Total views: 6111 | Total downloads: 3594

DBG