Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach

Iacopo Marri, Emil Petkovski, Loredana Cristaldi () and Marco Faifer
Additional contact information
Iacopo Marri: Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Emil Petkovski: Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Loredana Cristaldi: Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Marco Faifer: Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy

Energies, 2023, vol. 16, issue 11, 1-13

Abstract: Lithium-ion batteries play a vital role in many systems and applications, making them the most commonly used battery energy storage systems. Optimizing their usage requires accurate state-of-health (SoH) estimation, which provides insight into the performance level of the battery and improves the precision of other diagnostic measures, such as state of charge. In this paper, the classical machine learning (ML) strategies of multiple linear and polynomial regression, support vector regression (SVR), and random forest are compared for the task of battery SoH estimation. These ML strategies were selected because they represent a good compromise between light computational effort, applicability, and accuracy of results. The best results were produced using SVR, followed closely by multiple linear regression. This paper also discusses the feature selection process based on the partial charging time between different voltage intervals and shows the linear dependence of these features with capacity reduction. The feature selection, parameter tuning, and performance evaluation of all models were completed using a dataset from the Prognostics Center of Excellence at NASA, considering three batteries in the dataset.

Keywords: lithium-ion battery; machine learning; SoH; battery degradation; prognostics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/11/4423/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/11/4423/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:11:p:4423-:d:1159892

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().