Optimal Sensor Placement for Multifault Detection and Isolation in Lithium-Ion Battery Pack

The work presented in this article is motivated by a project related to the electrification of commercial aircraft. Energy storage systems (ESSs) for hybrid-electric aircraft applications require the ability to provide accurate diagnoses to insure system availability and reliability. In aerospace applications, battery packs may consist of thousands of interconnected cells and the associated electrical/electronic hardware, which brings a series of challenges for designing the battery management system (BMS). This article uses the tools of structural analysis to determine the placement of sensors that are needed by the BMS to enable monitoring and fault diagnosis at the individual cell level. First, the degree of analytical redundancy (AR) in the battery system that can be used for diagnostic strategies is determined. Then, structural models of different battery pack architectures are used to study how different measurements (current, voltage, and temperature) may improve the ability to monitor and diagnose a battery system. Possible sensor placement strategies that would enable the diagnosis of individual sensor faults, individual cell faults, and connection faults for different battery pack topologies are analyzed as well. A software-in-the-loop (SIL) framework is utilized to validate the proposed approach.

Automated summary

This article investigates the diagnostic issues of energy storage systems in the electrification project of commercial aircraft. The placement of sensors for the battery management system is determined using structural analysis, and the impact of different measurements on the monitoring and diagnosis of battery systems is studied. The research findings can provide reliability assurance for battery systems.