TWAICE, the leading provider of battery analytics software, Electric Power Research Institute (EPRI) and Pacific Northwest National Laboratory (PNNL) published today their joint study: the most recent, comprehensive publicly available analysis of the root causes of battery energy storage system (BESS) failure incidents.
Battery energy storage system (BESS) failure is being investigated heavily because of how disastrous BESS failures can be, and how important BESS is to the future of the grid. A joint study commissioned to analyze root causes of BESS failures underlined the impact of battery monitoring more than battery cell defects.
The focus of the database is on lithium ion technologies, but other battery technology failure incidents are included. Failure incident: An occurrence caused by a BESS system or component failure which resulted in increased safety risk. For lithium ion BESS, this is typically a thermal risk such as fire or explosion.
These articles explain the background of Lithium-ion battery systems, key issues concerning the types of failure, and some guidance on how to identify the cause(s) of the failures. Failure can occur for a number of external reasons including physical damage and exposure to external heat, which can lead to thermal runaway.
Note that the Stationary Energy Storage Failure Incidents table tracks both utility-scale and C&I system failures. It is instructive to compare the number of failure incidents over time against the deployment of BESS. The graph to the right looks at the failure rate per cumulative deployed capacity, up to 12/31/2024.
Analytics software is ideally suited to detect these incidents before they lead to a system failure, and the publication of this report should help guide the development of mitigation strategies – which include the deployment of battery analytics. The full report can be downloaded at EPRI's website.
PoF is not the only type of physics-based approach to model battery failure modes, performance, and degradation process. Other physics-based models have similar issues in development as PoF, and as such they work best with support of empirical data to verify assumptions and tune the results.
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