  Estimating the State of Health (SOH) of batteries is crucial for ensuring the
reliable operation of battery systems. Since there is no practical way to
instantaneously measure it at run time, a model is required for its estimation.
Recently, several data-driven SOH models have been proposed, whose accuracy
heavily relies on the quality of the datasets used for their training. Since
these datasets are obtained from measurements, they are limited in the variety
of the charge/discharge profiles. To address this scarcity issue, we propose
generating datasets by simulating a traditional battery model (e.g., a
circuit-equivalent one). The primary advantage of this approach is the ability
to use a simulatable battery model to evaluate a potentially infinite number of
workload profiles for training the data-driven model. Furthermore, this general
concept can be applied using any simulatable battery model, providing a fine
spectrum of accuracy/complexity tradeoffs. Our results indicate that using
simulated data achieves reasonable accuracy in SOH estimation, with a 7.2%
error relative to the simulated model, in exchange for a 27X memory reduction
and a =2000X speedup.
