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AuthorSheikh, Shehzar Shahzad
AuthorAnjum, Mahnoor
AuthorKhan, Muhammad Abdullah
AuthorHassan, Syed Ali
AuthorKhalid, Hassan Abdullah
AuthorGastli, Adel
AuthorBen-Brahim, Lazhar
Available date2022-11-15T11:34:39Z
Publication Date2020-07-15
Publication NameEnergies
Identifierhttp://dx.doi.org/10.3390/en13143658
CitationSheikh, S. S., Anjum, M., Khan, M. A., Hassan, S. A., Khalid, H. A., Gastli, A., & Ben-Brahim, L. (2020). A battery health monitoring method using machine learning: A data-driven approach. Energies, 13(14), 3658.
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090506584&origin=inward
URIhttp://hdl.handle.net/10576/36461
AbstractBatteries 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.
Languageen
PublisherMDPI
SubjectBattery health monitoring
Feature extraction
Knee-point calculation
Machine learning
State of health
TitleA battery health monitoring method using machine learning: A data-driven approach
TypeArticle
Issue Number14
Volume Number13
ESSN1996-1073


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