In electric vehicles (EV), load profiles consist of oscillations, pulses and rests . On the one hand, several studies have investigated current profiles with alternating current frequencies, usually above 10 Hz . At these frequencies, the electrochemical double layer is charged and discharged, but no Faradaic processes occur, thereby limiting degradation . On the other hand, regenerative braking, driving in stop-and-go traffic, etc. occur at substantially lower frequencies , but are not well understood . Additionally, time-induced aging (including calendar aging at zero current ) is another critical component of realistic usage but requires several years of experiments before being observed. Therefore, a gap exists at the intersection of data-driven approaches and battery aging experiments with realistic discharge protocols. We aim to fill this gap by generating and analyzing a non-accelerated and dynamically-cycled battery dataset that represents realistic EV driving.