Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization

Diego Castanho, Marcio Guerreiro, Ludmila Silva, Jony Eckert, Thiago Antonini Alves, Yara de Souza Tadano, Sergio Luiz Stevan, Hugo Valadares Siqueira () and Fernanda Cristina Corrêa
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Diego Castanho: Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil
Marcio Guerreiro: Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil
Ludmila Silva: Graduate Program in Mechanical Engineering, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil
Jony Eckert: Graduate Program in Mechanical Engineering, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil
Thiago Antonini Alves: Graduate Program in Mechanical Engineering (PPGEM), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil
Yara de Souza Tadano: Graduate Program in Mechanical Engineering (PPGEM), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil
Sergio Luiz Stevan: Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil
Hugo Valadares Siqueira: Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil
Fernanda Cristina Corrêa: Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology—Paraná (UTFPR), R. Doutor Washington Subtil Chueire, 330—Jardim Carvalho, Ponta Grossa 84017-220, PR, Brazil

Energies, 2022, vol. 15, issue 19, 1-21

Abstract: 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.

Keywords: state of charge; lithium-ion battery; computational intelligence; electric vehicle; MLR (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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