LAPSE:2023.24262
Published Article
LAPSE:2023.24262
A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach
March 27, 2023
Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures.
Keywords
battery health monitoring, feature extraction, knee-point calculation, Machine Learning, state of health
Suggested Citation
Sheikh SS, Anjum M, Khan MA, Hassan SA, Khalid HA, Gastli A, Ben-Brahim L. A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach. (2023). LAPSE:2023.24262
Author Affiliations
Sheikh SS: U.S.-Pakistan Center for Advanced Studies in Energy, (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan [ORCID]
Anjum M: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan [ORCID]
Khan MA: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan [ORCID]
Hassan SA: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan [ORCID]
Khalid HA: U.S.-Pakistan Center for Advanced Studies in Energy, (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan [ORCID]
Gastli A: Department of Electrical Engineering, Qatar University (QU), Doha 2713, Qatar [ORCID]
Ben-Brahim L: Department of Electrical Engineering, Qatar University (QU), Doha 2713, Qatar [ORCID]
Journal Name
Energies
Volume
13
Issue
14
Article Number
E3658
Year
2020
Publication Date
2020-07-15
Published Version
ISSN
1996-1073
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Original Submission
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PII: en13143658, Publication Type: Journal Article
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LAPSE:2023.24262
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doi:10.3390/en13143658
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Mar 27, 2023
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