There are many use cases for battery lifetime prediction ML models, and the model development is specific to each use case. In Figure , we provide an overview of several typical use cases and classify them based on whether the cycling conditions are varied when carrying out the cycling experiments. For example, a battery engineer who aims to optimize the cell design will cycle batteries identically for a fair comparison (Figure scenario a) . Alternatively, a model sorting short-lasting batteries out of a production line needs to rely on minimal testing as batteries cannot be cycled more than a few times (Figure scenario b) . In another scenario, a battery engineer aims to determine the impact of usage conditions, such as the depth of discharge and the charging current, on battery lifetime . For such a use case, an experiment with intentionally varied aging conditions is needed to build a prognosis model (Figure ). Such a model could be further integrated into more complex architectures to optimize over a large protocol parameter space (Figure scenario d) . In another use case, an electric vehicle (EV) engineer aims to integrate a prediction model in the battery management system (BMS) to estimate the battery state-of-health (SOH) . Contrary to the previous scenario, cells are cycled variably during driving and the BMS can access historical data for predictions (Figure scenario e). Lastly, for battery repurposing, models do not have access to historical cycling data (Figure scenario f) . These use cases are diverse and involve cycling conditions which are either constant or variable. Varying the cycling conditions is an artificial yet convenient way to enhance the diversity of the dataset and decrease the number of cells that need to be tested. This approach of diversifying the dataset via variable cycling conditions is analogous to accelerating aging via elevated temperature cycling, with the goal being to decrease the resources (number of cells and time) required.