The tenant receives the capacity trajectory of at least the first 40 cycles along with the overpotential difference between the 10 th and 40 th cycle, the Coulombic efficiency, and the variance between charge and discharge capacities . Based on this input the model predicts the complete cell trajectory. The EOL is calculated as the point at which the cell capacity goes below 80 % of the initial capacity. To capture uncertainty, we use an ensemble of five models and extend the LSTM architecture to predict the variance of the output along with the mean. If the cell chemistry and format from which training data are obtained differ from the production data, the model predictions can become unreliable. Mitigating this risk, the model is trained with a dataset of 44 cells obtained from reference after filtering out outliers. This data was generated using the same cell type, hardware, and cycling protocol as the Cycler tenant. The uncertainty output gives an indication of the prediction accuracy. The code for this tenant was developed in PyTorch and is available online: .