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

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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
    These authors contributed equally to this work.)

  • 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
    These authors contributed equally to this work.)

  • Ludmila Silva

    (Graduate Program in Mechanical Engineering, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil
    These authors contributed equally to this work.)

  • Jony Eckert

    (Graduate Program in Mechanical Engineering, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil
    These authors contributed equally to this work.)

  • 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
    These authors contributed equally to this work.)

  • 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
    These authors contributed equally to this work.)

  • 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
    These authors contributed equally to this work.)

  • 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
    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
    These authors contributed equally to this work.)

  • 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
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Diego Castanho & Marcio Guerreiro & Ludmila Silva & Jony Eckert & Thiago Antonini Alves & Yara de Souza Tadano & Sergio Luiz Stevan & Hugo Valadares Siqueira & Fernanda Cristina Corrêa, 2022. "Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization," Energies, MDPI, vol. 15(19), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6881-:d:919983
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    References listed on IDEAS

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    1. Cheng, Yujie & Song, Dengwei & Wang, Zhenya & Lu, Chen & Zerhouni, Noureddine, 2020. "An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation," Applied Energy, Elsevier, vol. 266(C).
    2. Li, Gaopeng & Zhang, Jieli & He, Hongwen, 2017. "Battery SOC constraint comparison for predictive energy management of plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 194(C), pages 578-587.
    3. Li, Yuanyuan & Sheng, Hanmin & Cheng, Yuhua & Stroe, Daniel-Ioan & Teodorescu, Remus, 2020. "State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis," Applied Energy, Elsevier, vol. 277(C).
    4. Zhao, Dao & Zhou, Zhijie & Tang, Shuaiwen & Cao, You & Wang, Jie & Zhang, Peng & Zhang, Yijun, 2022. "Online estimation of satellite lithium-ion battery capacity based on approximate belief rule base and hidden Markov model," Energy, Elsevier, vol. 256(C).
    5. Barbosa, Társis Prado & Eckert, Jony Javorski & Roso, Vinícius Rückert & Pujatti, Fabrício José Pacheco & da Silva, Leonardo Adolpho Rodrigues & Horta Gutiérrez, Juan Carlos, 2021. "Fuel saving and lower pollutants emissions using an ethanol-fueled engine in a hydraulic hybrid passengers vehicle," Energy, Elsevier, vol. 235(C).
    6. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    7. Hugo Siqueira & Mariana Macedo & Yara de Souza Tadano & Thiago Antonini Alves & Sergio L. Stevan & Domingos S. Oliveira & Manoel H.N. Marinho & Paulo S.G. de Mattos Neto & João F. L. de Oliveira & Ive, 2020. "Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods," Energies, MDPI, vol. 13(16), pages 1-35, August.
    8. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).
    9. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    10. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    11. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
    12. Veneri, Ottorino & Capasso, Clemente & Patalano, Stanislao, 2018. "Experimental investigation into the effectiveness of a super-capacitor based hybrid energy storage system for urban commercial vehicles," Applied Energy, Elsevier, vol. 227(C), pages 312-323.
    13. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    14. Hou, Hui & Xue, Mengya & Xu, Yan & Xiao, Zhenfeng & Deng, Xiangtian & Xu, Tao & Liu, Peng & Cui, Rongjian, 2020. "Multi-objective economic dispatch of a microgrid considering electric vehicle and transferable load," Applied Energy, Elsevier, vol. 262(C).
    15. Sarvaiya, Shradhdha & Ganesh, Sachin & Xu, Bin, 2021. "Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life," Energy, Elsevier, vol. 228(C).
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    Cited by:

    1. Carlos Henrique Illa Font & Hugo Valadares Siqueira & João Eustáquio Machado Neto & João Lucas Ferreira dos Santos & Sergio Luiz Stevan & Attilio Converti & Fernanda Cristina Corrêa, 2023. "Second Life of Lithium-Ion Batteries of Electric Vehicles: A Short Review and Perspectives," Energies, MDPI, vol. 16(2), pages 1-14, January.
    2. Tadeusz Białoń & Roman Niestrój & Wojciech Skarka & Wojciech Korski, 2023. "HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example," Energies, MDPI, vol. 16(17), pages 1-21, August.
    3. Tadeusz Białoń & Roman Niestrój & Wojciech Korski, 2023. "PSO-Based Identification of the Li-Ion Battery Cell Parameters," Energies, MDPI, vol. 16(10), pages 1-22, May.

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