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dc.contributor.authorMian Qaisar, Saeed
dc.contributor.authorAbdelGawad, Amal
dc.date.accessioned2022-11-03T11:57:25Z
dc.date.available2022-11-03T11:57:25Z
dc.identifier.citationS. M. Qaisar and A. E. E. AbdelGawad, "Prediction of the Li-Ion Battery Capacity by Using Event-Driven Acquisition and Machine Learning," 2021 7th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP), 2021, pp. 1-6, doi: 10.1109/EBCCSP53293.2021.9502399
dc.identifier.doihttps://doi.org/10.1016/j.jksuci.2022.05.009
dc.identifier.urihttp://hdl.handle.net/20.500.14131/170
dc.description.abstractThe battery is a crucial element of modern power systems and it is utilized habitually in different vital applications such as electric vehicles, drones, avionics and mobile phones. Among various batteries technologies the Li-Ion batteries are widely used. It is mainly because of their compactness, longer life and high power capacity. On the other hand, due to the disadvantage of Li-ion batteries being expensive, their use is monitored using battery management systems (BMSs) to optimize their performance and ensure they last longer. The extensive processing resources that modern BMSs need can result in higher overhead power consumption. This study focuses on upgrading the present Li-ion BMSs through redesigning their associative data acquisition and processing chains differently. It aims at enhancing the data acquisition and estimation mechanisms for the Li-ion batteries' capacities. It utilizes a novel event-driven mechanism for extracting the intended Li-Ion cell parameters. The event-driven approach brings notable compression gain compared to fix-rate conventional counterparts. The mined attributes are onward conveyed to the robust machine learning algorithms for prediction. The 5-fold cross-validation approach is used for prediction performance evaluation. The achieved correlation coefficient and minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are respectively 0.9996, 0.0038 and 0.0054 respectively. It shows the feasibility of incorporating the proposed approach in contemporary BMSs.
dc.language.isoen_US
dc.publisherIEEE
dc.titlePrediction of the Li-Ion Battery Capacity by Using Event-Driven Acquisition and Machine Learning
dc.typeBook chapter
dc.contributor.researcherElectrical and Computer Engineering


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