A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach. Sheikh, S. S., Anjum, M., Khan, M. A., Hassan, S. A., Khalid, H. A., Gastli, A., & Ben-Brahim, L. Energies, 13(14):3658, January, 2020. Number: 14 Publisher: Multidisciplinary Digital Publishing Institute![A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{sheikh_battery_2020,
title = {A {Battery} {Health} {Monitoring} {Method} {Using} {Machine} {Learning}: {A} {Data}-{Driven} {Approach}},
volume = {13},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {1996-1073},
shorttitle = {A {Battery} {Health} {Monitoring} {Method} {Using} {Machine} {Learning}},
url = {https://www.mdpi.com/1996-1073/13/14/3658},
doi = {10.3390/en13143658},
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.},
language = {en},
number = {14},
urldate = {2022-02-09},
journal = {Energies},
author = {Sheikh, Shehzar Shahzad and Anjum, Mahnoor and Khan, Muhammad Abdullah and Hassan, Syed Ali and Khalid, Hassan Abdullah and Gastli, Adel and Ben-Brahim, Lazhar},
month = jan,
year = {2020},
note = {Number: 14
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {battery health monitoring, ecml, feature extraction, knee-point calculation, machine learning, state of health},
pages = {3658},
}
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