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المؤلفSheikh, Shehzar Shahzad
المؤلفAnjum, Mahnoor
المؤلفKhan, Muhammad Abdullah
المؤلفHassan, Syed Ali
المؤلفKhalid, Hassan Abdullah
المؤلفGastli, Adel
المؤلفBen-Brahim, Lazhar
تاريخ الإتاحة2022-11-15T11:34:39Z
تاريخ النشر2020-07-15
اسم المنشورEnergies
المعرّفhttp://dx.doi.org/10.3390/en13143658
الاقتباسSheikh, 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.
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090506584&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36461
الملخصBatteries 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.
اللغةen
الناشرMDPI
الموضوعBattery health monitoring
Feature extraction
Knee-point calculation
Machine learning
State of health
العنوانA battery health monitoring method using machine learning: A data-driven approach
النوعArticle
رقم العدد14
رقم المجلد13
ESSN1996-1073
dc.accessType Open Access


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