Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries
Joelton Deonei Gotz,
José Rodolfo Galvão,
Fernanda Cristina Corrêa,
Alceu André Badin,
Hugo Valadares Siqueira,
Emilson Ribeiro Viana,
Attilio Converti,
Milton Borsato
Affiliations
Joelton Deonei Gotz
Graduate Program in Electrical and Computer Engineering (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR-CT), Curitiba 80230-901, Brazil
José Rodolfo Galvão
Graduate Program in Electrical Engineering, Universidade Tecnológica Federal do Paraná (UTFPR-PG), Ponta Grossa 84017-220, Brazil
Fernanda Cristina Corrêa
Graduate Program in Electrical Engineering, Universidade Tecnológica Federal do Paraná (UTFPR-PG), Ponta Grossa 84017-220, Brazil
Alceu André Badin
Graduate Program in Electrical and Computer Engineering (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR-CT), Curitiba 80230-901, Brazil
Hugo Valadares Siqueira
Graduate Program in Electrical Engineering, Universidade Tecnológica Federal do Paraná (UTFPR-PG), Ponta Grossa 84017-220, Brazil
Emilson Ribeiro Viana
Physics Department, Universidade Tecnológica Federal do Paraná (UTFPR-CT), Curitiba 80230-901, Brazil
Attilio Converti
Department of Civil, Chemical and Environmental Engineering, University of Genoa, Pole of Chemical Engineering, Via Opera Pia 15, 16145 Genoa, Italy
Milton Borsato
Postgraduate Program in Mechanical and Materials Engineering (PPGEM), Universidade Tecnológica Federal do Paraná (UTFPR-CT), Curitiba 81280-340, Brazil
Retired batteries pose a significant current and future challenge for electric mobility due to their high cost and the need for a state of health (SOH) above 80% to supply energy efficiently. Recycling and alternative applications are the primary options for these batteries, with recycling still undergoing research as regards more efficient and cost-effective techniques. While advancements have been made, researchers are actively seeking improved methods. Repurposing retired batteries for lower-performance applications like stationary systems or low-speed vehicles is recommended. Second-life batteries (SLB) can be directly reused or reconstructed, with the latter involving the disassembly, measurement, and separation of cells based on their characteristics. The traditional measurement process, involving full charge and discharge cycles, is time-consuming. To address this, a Machine Learning (ML)-based SOH estimator is introduced in this work, offering the instant measurement and estimation of battery health without complete discharge. The results indicate that the model can accurately identify SOH within a nominal capacity range of 1400–2300 mAh, with a resolution near 45.70 mAh, in under five minutes of discharging. This innovative technique could be instrumental in selecting and assembling SLB packs.
