  The widespread adoption of Electric Vehicles (EVs) is limited by their
reliance on batteries with presently low energy and power densities compared to
liquid fuels and are subject to aging and performance deterioration over time.
For this reason, monitoring the battery State Of Charge (SOC) and State Of
Health (SOH) during the EV lifetime is a very relevant problem. This work
proposes a battery digital twin structure designed to accurately reflect
battery dynamics at the run time. To ensure a high degree of correctness
concerning non-linear phenomena, the digital twin relies on data-driven models
trained on traces of battery evolution over time: a SOH model, repeatedly
executed to estimate the degradation of maximum battery capacity, and a SOC
model, retrained periodically to reflect the impact of aging. The proposed
digital twin structure will be exemplified on a public dataset to motivate its
adoption and prove its effectiveness, with high accuracy and inference and
retraining times compatible with onboard execution.
