During the course of battery operation, assessing the performance and health of a cell is a challenging task, normally addressed by intermittent diagnostic tests. Various types of diagnostic tests are performed non-destructively to evaluate the ability of a battery to store and release energy, typically after a battery has undergone significant electrochemical cycling . High-precision coulometry tests can infer different degradation modes from measurements , but require equipment with extreme measurement precision. Reference performance tests (RPT) measure the ability of a battery to charge and discharge a specific current . Electrochemical impedance spectroscopy (EIS) is another commonly used method, which quantifies the frequency-dependent response to small AC perturbations . Hybrid pulse power characterization (HPPC) tests utilize large intermittent current pulses and rest periods to capture the behavior of a cell under high currents far from equilibrium . All of these tests can be leveraged for interpretable machine learning of battery degradation, based on cell-level SoH and performance, such as internal resistance and accessible capacity , but connections to microscopic kinetic parameters could lead to more robust feature engineering and insights into the internal state of degradation.