Online fault detection and fault tolerance in electrical energy storage systems

Abstract

Electrical energy storage (EES) systems have broad application in portable electronic devices, electrical vehicles, data centers, etc. Faulty EES elements, i.e., open-circuited or short-circuited EES elements, which result in a shortening of the system lifetime, are inevitable especially for long-term use of EES systems. Manual EES element fault detection and elimination incur significant overhead and require the EES system to stop operation during the fault detection process. Therefore, online fault detection and tolerance methods without manual intervention provide great benefit in operability of the EES system. Existing EES system diagnosis techniques have limitations in that (i) they only focus on battery arrays and rely on battery management units for fault detection, and (ii) they lack systematic online fault tolerance architecture. In this paper, a framework of online fault detection and tolerance for supercapacitor EES arrays is presented. Precisely, an enhanced EES array reconfiguration architecture that serves as the hardware support is described, and efficient algorithms for online fault detection and tolerance are presented. The proposed fault detection algorithm identifies faulty supercapacitors with logarithmic time complexity. The fault tolerance algorithm bypasses faulty supercapacitors and produces the optimal configuration of the supercapacitor array considering the converter efficiency variation. The proposed method is not limited to a supercapacitor array. Experimental results demonstrate that the proposed fault detection and tolerance technique reduces the fault-induced EES system performance degradation by up to 91%.