Active Model-Based Fault Diagnosis in Reconfigurable Battery Systems

With the increasing demand for electric vehicles, the interest in battery systems is growing. In order to enable safe operation of these complex energy storage systems, methods of fault diagnosis are needed. Particularly, reconfigurable battery systems (RBSs) with switches are promising on the way to fault tolerance as they allow the system to be reconfigured in the event of a fault. In this article, a model-based fault diagnosis algorithm is developed and validated that uses the switches of an RBS to improve the fault isolability. Since the algorithm changes the structure of the system in order to differentiate between nonisolable faults, it is classified as an active fault diagnosis algorithm. The deviations between sensor measurements and model, called residuals, are stochastically analyzed. For fault isolation, a fuzzy clustering approach is used. A constrained sigma-point Kalman filter minimizes model uncertainties and therefore increases the sensitivity and robustness of the fault diagnosis approach. Furthermore, the filter allows estimating the fault amplitude in case of a fault. Based on active sequential hypothesis testing, a policy to calculate the next switch position is proposed and investigated. It is shown simulatively and experimentally that additional faults are isolated by the presented active approach.

Automated summary

This article develops and validates a model-based fault diagnosis algorithm that utilizes the switches of an RBS to improve fault isolability, utilizes a fuzzy clustering approach for fault isolation, and enhances sensitivity and robustness of the fault diagnosis method with a constrained sigma-point Kalman filter.