Lithium-ion batteries soh estimation, based on support-vector regression and a feature-based approach

Iacopo Marri, Emil Petkovski,Loredana Cristaldi, Marco Faifer

Abstract

Lithium-Ion batteries, have become enormously used in many systems and applications, and are the most widespread energy storage system. Optimizing the usage of batterie is therefore very important to increase the safety of systems like electric vehicles or portable devices, to reduce economic loss in industrial environments, and to increase their availability. An accurate State of Health (SoH) estimation is important since it allows us to know battery conditions and make an appropriate use of it, and it improves the accuracy of other diagnostic measures, like State of Charge (SoC). In this paper, an approach for SoH estimation is proposed, based on Support Vector Regression machine learning algorithm and a smart feature extraction process, finding a good trade-off between applicability, light computation effort, and accuracy of results. Features selection and parameters tuning are discussed, and performances are measured on a dataset from the Prognostics Center of Excellence at NASA, considering 3 batteries of the dataset.

Keywords

SoH; machine learning; lithium-ion batteries; degradation diagnostic

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IMEKO-TC10-2022-021.pdf

DOI

10.21014/tc10-2022.021

IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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