Safety hazards resulting from distributed energy storage devices (e.g., lithium ion battery) failures range from reduced performance to operational impairment. Therefore, fault diagnostics is a very important function of distributed energy storage management systems such as battery management systems. Real time diagnostics of the failure and early warning of these faults can mitigate potential catastrophic events in electric vehicles, community energy storage devices as well as grid-scale energy storage systems. This team has developed a technology for the differentiated diagnosis and early detection of battery faults, including internal and external short circuit faults, over charge, over discharge, and over temperature. The differentiated diagnostic algorithms can be further implemented in the future generation of distributed energy management systems for real time fault diagnosis and prevention.
With rapidly evolving technologies of electric vehicles, consumer electronics (e.g., cell phones, computers), aircraft and aviation, the battery has emerged as the most prominent energy storage device. It is important to improve the performance of the battery management system to make the battery a safe, reliable, and cost efficient solution. The developed technology, when successfully commercialized, will provide intelligent, fast monitoring, estimation, and management for distributed energy storage devices at a cost acceptable to the market place; help prevent catastrophic events; and translate the legacy Centralized management for a cluster of distributed energy storage devices to a completely distributed architecture by only exchanging local information with neighbors.
