Abstract
Performance of the current battery management systems is limited by the on-board embedded systems as the number of battery cells increases in the large-scale lithium-ion (Li-ion) battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based battery condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based battery fault diagnosis algorithm are built in the cloud battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective maintenance of the large-scale Li-ion BESSs.
Highlights
Lithium-ion (Li-ion) batteries are excellent power source and energy storage devices used in various electrical and electronic systems due to high power and energy density, low maintenance requirement, low self-discharge, and no memory effect [1]
This paper proposes a novel cloud-based battery condition monitoring and fault diagnosis platform, which incorporates Internet of Things (IoT)-enabled wireless battery module management systems (WMMS)
A hybrid filter (HF)-based condition monitoring incorporating a real-time battery model developed by authors [42] and the proposed monitoring incorporating a real-time battery model developed by authors [42] and the proposed outlier mining-based fault diagnosis algorithm are applied in the cloud battery management platform (CBMP)
Summary
Lithium-ion (Li-ion) batteries are excellent power source and energy storage devices used in various electrical and electronic systems due to high power and energy density, low maintenance requirement, low self-discharge, and no memory effect [1]. The proposed cloud battery management platform (CBMP) to support on-board battery health monitoring and to provide intelligent and cost-effective maintenance of the large-scale BESSs. The hybrid filter (HF)-based condition monitoring algorithm of cells and the proposed outlier detection-based fault diagnosis algorithm are implemented in the CBMP. To the authors’ best knowledge, the investigation and actual implementation of the cloud-based battery health monitoring platform for the large-scale BESSs has not been studied yet, which supports on-board battery health monitoring and provides tools that will enable a new level of intelligent and cost-effective maintenance in cyber-physical environments replacing an expensive supervisory control and data acquisition system toward the generation BMS. Providing such a tractable and practical cyber-physical platform for large-scale Li BESSs is the main contribution of this paper
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