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 Jr; Hugo Valadares Siqueira; Fernanda Cristina Corrêa

LAPSE:2023.8990

Published Article

LAPSE:2023.8990

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 Jr, Hugo Valadares Siqueira, Fernanda Cristina Corrêa

February 27, 2023

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.

Record ID

LAPSE:2023.8990

Keywords

computational intelligence, electric vehicle, lithium-ion battery, MLR, state of charge

Subject

Optimization

Suggested Citation

Castanho D, Guerreiro M, Silva L, Eckert J, Antonini Alves T, Tadano YDS, Stevan SL Jr, Siqueira HV, Corrêa FC. Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization. (2023). LAPSE:2023.8990

Author Affiliations

Castanho D: 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 [ORCID]
Guerreiro M: 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 [ORCID]
Silva L: Graduate Program in Mechanical Engineering, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil [ORCID]
Eckert J: Graduate Program in Mechanical Engineering, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil [ORCID]
Antonini Alves T: 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 [ORCID]
Tadano YDS: 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 [ORCID]
Stevan SL Jr: 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 [ORCID]
Siqueira HV: 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; Graduate Program in Industrial Engineering (PPGEP), Fed [ORCID]
Corrêa FC: 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 [ORCID]

Journal Name

Energies

Volume

15

Issue

19

First Page

6881

Year

2022

Publication Date

2022-09-20