Event-Driven Acquisition and Machine-Learning-Based Efficient Prediction of the Li-Ion Battery Capacity

Saeed Mian Qaisar; Amal Essam ElDin AbdelGawad; Kathiravan Srinivasan

doi:10.1007/s42979-021-00905-0

Event-Driven Acquisition and Machine-Learning-Based Efficient Prediction of the Li-Ion Battery Capacity

SN Comput Sci. 2022;3(1):15. doi: 10.1007/s42979-021-00905-0. Epub 2021 Oct 26.

Authors

Saeed Mian Qaisar^{1

2}, Amal Essam ElDin AbdelGawad¹, Kathiravan Srinivasan³

Affiliations

¹ College of Engineering, Effat University, Jeddah, 22332 Saudi Arabia.
² Communication and Signal Processing Lab, Energy and Technology Research Center, Effat University, Jeddah, 22332 Saudi Arabia.
³ School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, India.

Abstract

In almost all contemporary power systems, the battery is an elementary component, and it is routinely used in a variety of critical applications such as drones, avionics, and cell phones. Due to their superior characteristics compared to the concurrent technologies, Li-ion batteries are widely utilized. Since batteries are costly, their usage is closely monitored by battery management systems (BMSs). It ensures that batteries survive and serve longer. Modern BMSs' are complex and sophisticated and can deal with hundreds of cells in a battery pack. It results in an increased processing resources requirement and can cause an overhead power consumption. The aim of this work is to improve current BMSs by redesigning their associative processing chain. It focuses on improving data collection, processing and prediction processes for Li-ion battery cell capacities. To prevent the processing of a large amount of unnecessary data, the classical sensing approach that is fix-rate is avoided and replaced by event-driven sensing (EDS) mechanism to digitize battery cell parameters such as voltages, currents, and temperatures in a way that allows for real-time data compressing. A new approach is proposed for event-driven feature extraction. The robust machine-learning algorithms are employed for processing the extracted features and to predict the capacity of considered battery cell. Results show a considerable compression gain with a correlation coefficient of 0.999 and the relative absolute error (RAE) and root relative squared error (RRSE) of 1.88% and 2.08%, respectively.

Keywords: Battery capacity; Computational complexity; Event-driven processing; Features extraction; Li-ion battery; Remaining useful life.