Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study
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 hy...
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| Format: | Article |
| Language: | English |
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Indonesian Institute of Sciences
2017-07-01
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| Series: | Journal of Mechatronics, Electrical Power, and Vehicular Technology |
| Subjects: | |
| Online Access: | https://mev.lipi.go.id/mev/article/view/369 |
| _version_ | 1823968166140706816 |
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| author | Latif Rozaqi Estiko Rijanto Stratis Kanarachos |
| author_facet | Latif Rozaqi Estiko Rijanto Stratis Kanarachos |
| author_sort | Latif Rozaqi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T18:37:52Z |
| format | Article |
| id | doaj.art-3412226cd36b4e2180e81cee1dd8b4dc |
| institution | Directory Open Access Journal |
| issn | 2087-3379 2088-6985 |
| language | English |
| last_indexed | 2024-12-17T18:37:52Z |
| publishDate | 2017-07-01 |
| publisher | Indonesian Institute of Sciences |
| record_format | Article |
| series | Journal of Mechatronics, Electrical Power, and Vehicular Technology |
| spelling | doaj.art-3412226cd36b4e2180e81cee1dd8b4dc2022-12-21T21:37:03ZengIndonesian Institute of SciencesJournal of Mechatronics, Electrical Power, and Vehicular Technology2087-33792088-69852017-07-0181404910.14203/j.mev.2017.v8.40-49179Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation studyLatif Rozaqi0Estiko Rijanto1Stratis Kanarachos2Indonesian Institute of SciencesIndonesian Institute of SciencesCoventry UniversityThis 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.https://mev.lipi.go.id/mev/article/view/369li-ionbatterystate of charge (soc)state of health (soh)recursive least square (rls)particle swarm optimization (pso)genetic algorithm (ga) |
| spellingShingle | Latif Rozaqi Estiko Rijanto Stratis Kanarachos Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study Journal of Mechatronics, Electrical Power, and Vehicular Technology li-ion battery state of charge (soc) state of health (soh) recursive least square (rls) particle swarm optimization (pso) genetic algorithm (ga) |
| title | Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study |
| title_full | Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study |
| title_fullStr | Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study |
| title_full_unstemmed | Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study |
| title_short | Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study |
| title_sort | comparison between rls ga and rls pso for li ion battery soc and soh estimation a simulation study |
| topic | li-ion battery state of charge (soc) state of health (soh) recursive least square (rls) particle swarm optimization (pso) genetic algorithm (ga) |
| url | https://mev.lipi.go.id/mev/article/view/369 |
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