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Rozaqi, Latif
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Electrical & Electronics Engineering

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Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study Rozaqi, Latif; Rijanto, Estiko; Kanarachos, Stratis
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 8, No 1 (2017)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (226.024 KB) | DOI: 10.14203/j.mev.2017.v8.40-49

Abstract

This paper proposes a new method of concurrent SOC and SOH estimation using a combination of recursive least square (RLS) algorithm and particle swarm optimization (PSO). The RLS algorithm is equipped with multiple fixed forgetting factors (MFFF) which are optimized by PSO. The performance of the hybrid RLS-PSO is compared with the similar RLS which is optimized by single objective genetic algorithms (SOGA) as well as multi-objectives genetic algorithm (MOGA). Open circuit voltage (OCV) is treated as a parameter to be estimated at the same timewith internal resistance. Urban Dynamometer Driving Schedule (UDDS) is used as the input data. Simulation results show that the hybrid RLS-PSO algorithm provides little better performance than the hybrid RLS-SOGA algorithm in terms of mean square error (MSE) and a number of iteration. On the other hand, MOGA provides Pareto front containing optimum solutions where a specific solution can be selected to have OCV MSE performance as good as PSO.
Co-Authors Estiko Rijanto Kanarachos, Stratis