Comparison between RLS-GA and RLS-PSO For Li-ion battery SOC and SOH estimation: a simulation study

Latief Rozaqi, Estiko Rijanto, Stratis Kanarachos

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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.
Original languageEnglish
Pages (from-to)40-49
Number of pages10
JournalJournal of Mechatronics, Electrical Power, and Vehicular Technology
Issue number1
Early online date31 Jul 2017
Publication statusPublished - Aug 2017

Bibliographical note

Publisher Statement: This is an open access article under the CC BY-NC-SA license(


  • Li-Ion
  • genetic algorithm (GA)
  • particle swarm optimization (PSO)
  • recursive least square (RLS)
  • state of health (SOH)
  • state of charge (SOC)
  • battery


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