Accurate State of Health (SoH) estimation is indispensable for ensuring battery system safety, reliability, and run time monitoring. However, as instantaneous runtime measurement of SoH remains impractical when not unfeasible, appropriate models are required for its estimation. Recently, various data-driven models have been proposed, which solve various weaknesses of traditional models. However, the accuracy of data-driven models heavily depends on the quality of the training datasets, which usually contain data that are easy to measure but that are only partially or weakly related to the physical/chemical mechanisms that determine battery aging. In this study, we propose a novel feature engineering approach, which involves augmenting the original dataset with purpose-designed features that better represent the aging phenomena. Our contribution does not consist of a new machine-learning model but rather in the addition of selected features to an existing model. This methodology consistently demonstrates enhanced accuracy across various machine-learning models and battery chemistries, yielding an approximate 25% SoH estimation accuracy improvement. Our work bridges a critical gap in battery research, offering a promising strategy to significantly enhance SoH estimation by optimizing feature selection.

Model-Driven Feature Engineering for Data-Driven Battery SOH Model / Alamin, Khaled; JAHIER PAGLIARI, Daniele; Chen, Yukai; Macii, Enrico; Vinco, Sara; Poncino, Massimo. - STAMPA. - (2024). (Intervento presentato al convegno Design, Automation and Test in Europe Conference 2024 tenutosi a Valencia (ESP) nel 25-27 March 2024).

Model-Driven Feature Engineering for Data-Driven Battery SOH Model

Khaled Alamin;Daniele Jahier Pagliari;Yukai Chen;Enrico Macii;Sara Vinco;Massimo Poncino
2024

Abstract

Accurate State of Health (SoH) estimation is indispensable for ensuring battery system safety, reliability, and run time monitoring. However, as instantaneous runtime measurement of SoH remains impractical when not unfeasible, appropriate models are required for its estimation. Recently, various data-driven models have been proposed, which solve various weaknesses of traditional models. However, the accuracy of data-driven models heavily depends on the quality of the training datasets, which usually contain data that are easy to measure but that are only partially or weakly related to the physical/chemical mechanisms that determine battery aging. In this study, we propose a novel feature engineering approach, which involves augmenting the original dataset with purpose-designed features that better represent the aging phenomena. Our contribution does not consist of a new machine-learning model but rather in the addition of selected features to an existing model. This methodology consistently demonstrates enhanced accuracy across various machine-learning models and battery chemistries, yielding an approximate 25% SoH estimation accuracy improvement. Our work bridges a critical gap in battery research, offering a promising strategy to significantly enhance SoH estimation by optimizing feature selection.
2024
978-3-9819263-8-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2985077