We generate training data by subjecting 24 Powerstream LiR2032 coin cells (of nominal capacity 1C = 35mAh) to a sequence of randomly selected charge and discharge currents at room temperature for 110-120 full charge/discharge cycles. Each cycle consists of an initial diagnosis of battery state, involving acquisition of the galvanostatic EIS spectrum, followed by usage, involving a charging and discharging stage. We collect impedance measurements at 57 frequencies uniformly distributed in the log domain in the range 0.02Hz-20kHz; a vector representation of battery state is formed from the real and imaginary parts of cell impedance at these frequencies. Charging consists of a two stage Constant Current (CC) protocol; currents are randomly selected in the ranges 70mA-140mA (2C-4C) and 35mA-105mA (1C-3C) in stages 1 and 2 respectively. If the safety threshold voltage of 4.3V is reached before the time limit then charging is stopped. During discharging, a single constant discharge current, randomly selected in the range 35mA-140mA (1C-4C), is applied, until the voltage drops to 3.0V. To test the model's robustness to domain shift, we additionally cycle 16 cells of the same chemistry and manufacturer under the same conditions as above, except now fixing the discharge current for all cells and cycles at 52.5mA (1.5C) instead of randomly changing the We first consider the setting in which we want to predict the next cycle discharge capacity, for a cell whose usage history (including for example, cycle or calendar age, or historical capacity-voltage data) is completely unknown, if we apply a particular charging and discharging profile. We frame the problem as a regression task, and train a probabilistic machine learning model to learn the mapping Figure illustrates the accuracy of our model. Using both state and action as input, the next cycle discharge capacity is predicted with an average error of 8.2%. Importantly, both state and action are found to be necessary to predict future cell performance: if state (3b) or action (3c) alone are used as inputs, the test error approximately doubles to 20.7% and 15.4% respectively. This demonstrates the importance of both the cell's internal health and the externally selected usage in determining realised cell performance.