Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization
Lithium-ion batteries are the current most promising device for electric vehicle applications. They have been widely used because of their advantageous features, such as high energy density, many cycles, and low self-discharge. One of the critical factors for the correct operation of an electric veh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/19/6881 |
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| author | Diego Castanho Marcio Guerreiro Ludmila Silva Jony Eckert Thiago Antonini Alves Yara de Souza Tadano Sergio Luiz Stevan Hugo Valadares Siqueira Fernanda Cristina Corrêa |
| author_facet | Diego Castanho Marcio Guerreiro Ludmila Silva Jony Eckert Thiago Antonini Alves Yara de Souza Tadano Sergio Luiz Stevan Hugo Valadares Siqueira Fernanda Cristina Corrêa |
| author_sort | Diego Castanho |
| collection | DOAJ |
| description | Lithium-ion batteries are the current most promising device for electric vehicle applications. They have been widely used because of their advantageous features, such as high energy density, many cycles, and low self-discharge. One of the critical factors for the correct operation of an electric vehicle is the estimation of the battery charge state. In this sense, this work presents a comparison of the state of charge estimation (SoC), tested in four different conduction profiles in different temperatures, which was performed using the Multiple Linear Regression without (MLR) and with spline interpolation (SPL-MLR) and the Generalized Linear Model (GLM). The models were calibrated by three different bio-inspired optimization techniques: Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The computational results showed that the MLR-PSO is the most suitable for SoC prediction, overcoming all other models and important proposals from the literature. |
| first_indexed | 2024-03-09T21:50:15Z |
| format | Article |
| id | doaj.art-ce7a421acf7e4f3296e029a231329612 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:50:15Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ce7a421acf7e4f3296e029a2313296122023-11-23T20:09:26ZengMDPI AGEnergies1996-10732022-09-011519688110.3390/en15196881Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm OptimizationDiego Castanho0Marcio Guerreiro1Ludmila Silva2Jony Eckert3Thiago Antonini Alves4Yara de Souza Tadano5Sergio Luiz Stevan6Hugo Valadares Siqueira7Fernanda Cristina Corrêa8Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, BrazilGraduate Program in Industrial Engineering (PPGEP), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, BrazilGraduate Program in Mechanical Engineering, University of Campinas (UNICAMP), Campinas 13083-970, SP, BrazilGraduate Program in Mechanical Engineering, University of Campinas (UNICAMP), Campinas 13083-970, SP, BrazilGraduate Program in Mechanical Engineering (PPGEM), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, BrazilGraduate Program in Mechanical Engineering (PPGEM), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, BrazilGraduate Program in Electrical Engineering (PPGEE), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, BrazilGraduate Program in Electrical Engineering (PPGEE), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, BrazilGraduate Program in Electrical Engineering (PPGEE), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, BrazilLithium-ion batteries are the current most promising device for electric vehicle applications. They have been widely used because of their advantageous features, such as high energy density, many cycles, and low self-discharge. One of the critical factors for the correct operation of an electric vehicle is the estimation of the battery charge state. In this sense, this work presents a comparison of the state of charge estimation (SoC), tested in four different conduction profiles in different temperatures, which was performed using the Multiple Linear Regression without (MLR) and with spline interpolation (SPL-MLR) and the Generalized Linear Model (GLM). The models were calibrated by three different bio-inspired optimization techniques: Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The computational results showed that the MLR-PSO is the most suitable for SoC prediction, overcoming all other models and important proposals from the literature.https://www.mdpi.com/1996-1073/15/19/6881state of chargelithium-ion batterycomputational intelligenceelectric vehicleMLR |
| spellingShingle | Diego Castanho Marcio Guerreiro Ludmila Silva Jony Eckert Thiago Antonini Alves Yara de Souza Tadano Sergio Luiz Stevan Hugo Valadares Siqueira Fernanda Cristina Corrêa Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization Energies state of charge lithium-ion battery computational intelligence electric vehicle MLR |
| title | Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization |
| title_full | Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization |
| title_fullStr | Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization |
| title_full_unstemmed | Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization |
| title_short | Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization |
| title_sort | method for soc estimation in lithium ion batteries based on multiple linear regression and particle swarm optimization |
| topic | state of charge lithium-ion battery computational intelligence electric vehicle MLR |
| url | https://www.mdpi.com/1996-1073/15/19/6881 |
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