Comparing life-cycle dynamics of Li-ion batteries (LIBs) clustered by operating conditions with SINDy

Lithium-ion batteries (LIBs) play a big part in the vision of a net-zero emission economy, yet it is commonly reported that only a small percentage of LIBs are recycled worldwide. An outstanding barrier to making recycling LIBs economical throughout the supply chain pertains to the uncertainty surrounding their remaining useful life (RUL). How do operating conditions impact initial useful life of the battery? We applied sparse identification of nonlinear dynamics method (SINDy) to understand the life-cycle dynamics of LIBs with respect to sensor data observed for current, voltage, internal resistance and temperature. A dataset of 124 commercial lithium iron phosphate/graphite (LFP) batteries have been charged and cycled to failure under 72 unique policies. Charging policies were standardized, reduced to PC scores, and clustered by a k-means algorithm. Sensor data from the first cycle was averaged within clusters, characterizing a "good as new" state. SINDy method was applied to discover dynamics of this state and compared amongst clusters. This work contributes to the effort of defining a model that can predict the remaining useful life (RUL) of LIBs during degradation.