Enhancing SOC estimation accuracy via incremental learning techniques for second-life batteries

Gotz, Joelton Deonei; de Souza, Paulo Henrique Garcia; Galvão, José Rodolfo; Corrêa, Fernanda Cristina; Badin, Alceu André; Viana, Emilson Ribeiro; Borsato, Milton

doi:10.1016/j.seta.2024.103753

Cited by 1 publication

(1 citation statement)

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“…Conversely, if the RF model combines all "weak" answers, it can yield a "strong" answer, reducing the chance of overfitting and bias. Besides that, compared to neural network models, RF is softer and requires fewer datasets and lower-powered computing resources [52][53][54][55].…”

Section: Resultsmentioning

confidence: 99%

Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries

Gotz,

Galvão,

Corrêa

et al. 2024

Vehicles

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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.

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Section: Resultsmentioning

confidence: 99%