A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

Shehzar Shahzad Sheikh, Mahnoor Anjum, Muhammad Abdullah Khan, Syed Ali Hassan, Hassan Abdullah Khalid, Adel Gastli and Lazhar Ben-Brahim
Additional contact information
Shehzar Shahzad Sheikh: U.S.-Pakistan Center for Advanced Studies in Energy, (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Mahnoor Anjum: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Muhammad Abdullah Khan: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Syed Ali Hassan: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Hassan Abdullah Khalid: U.S.-Pakistan Center for Advanced Studies in Energy, (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Adel Gastli: Department of Electrical Engineering, Qatar University (QU), Doha 2713, Qatar
Lazhar Ben-Brahim: Department of Electrical Engineering, Qatar University (QU), Doha 2713, Qatar

Energies, 2020, vol. 13, issue 14, 1-16

Abstract: 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 (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/14/3658/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/14/3658/ (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:13:y:2020:i:14:p:3658-:d:384986

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 ().