Big Data for the Diagnosis and Prognosis of Deployed Energy Storage Systems

The diagnosis and prognosis of deployed battery energy storage systems is difficult because the battery might never experience controlled conditions through normal operation as both the day-to-day charge and discharge duty cycles might be sporadic. To circumvent this issue new methodologies must be developed and we recently proposed one for photovoltaic connected batteries. The method is using real observed solar irradiance, modeled clear sky irradiance and synthetically generated battery data from a battery digital twin to diagnose the battery degradation. The approach was demonstrated to be effective for opportunistic diagnosis without the need for any maintenance cycles for nickel manganese cobalt oxide-based batteries for days with over 50% clear sky or with an average irradiance over 650 W/m
²
. This work is validating the approach further for lithium iron phosphate-based batteries, another of the main families of commercial lithium-ion batteries.