**Figure 4** presents a sketch of a lithium-ion battery's EIS plot. As battery excitation frequency increases from zero to infinity, one traces this EIS plot from the top right corner to the bottom left. The DFN model excels at matching the low-frequency portion of this plot. At very low frequencies, the battery acts as a pure integrator. The battery's output voltage at these frequencies is a function of state of charge, which is proportional to the integral of input current. The DFN model represents this fact through the static relationship between electrode equilibrium potentials and solid-phase ion concentrations. The EIS plot shows this integral behavior by curving upwards to become almost vertical at frequencies in the sub-mHz range. As excitation frequency increases, diffusion dynamics become relatively important. Consider Fick's law of diffusion for a spherical solid electrode particle. Fick's law can be discretized spatially to furnish a set of lumped-parameter state-space equations that physically represent cascades of resistive-capacitive dynamics. The corresponding EIS plot curves with frequency such that its slope approaches 45**̊**. This model-based insight is typically visible in an EIS plot, particularly as frequency increases from the mHz range to the Hz range. At higher excitation frequencies, the interfaces between the electrode particles, electrolyte, and current collectors become more important to model. Phenomena such as the double-layer capacitance of the SEI layer, the charge transfer resistance of the SEI layer, and the impedance of the passivation film at the heart of the SEI begin to reveal their own RC dynamics at the battery's input/output ports. We did not incorporate these RC dynamics in the DFN model presented here, but this can be done fairly easily, pushing the model's bandwidth to the neighborhood of 100Hz. As excitation frequencies push into the kHz range, inductive phenomena and *skin effects* come into play, causing the EIS plot to drop into the fourth quadrant.