Fault Diagnosis of Networked Battery Systems for Industrial Risk Control and Management

This work is the first of its kind to present a fractional-order learning observer for diagnosing individual battery faults in a battery string. There are no voltage measurements to each individual battery cell on this string; instead, a sensor is mounted at the two ends of the string to measure the total of the voltage values for all cells on the same string. To accurately model a battery cell, the constant phase element is used in the electric circuit model. Because it has the fractional-order characteristic, the battery cell is modeled as a fractional-order system. Based on the fractional-order model of the battery string, a fractional-order learning observer is designed to diagnose the faults from battery controller signals and internal short circuit to achieve industrial risk management. Simulation studies are conducted to verify the effectiveness of the proposed fractional-order learning observer.