The fundamental relationships between performance metrics, impedance spectra, and electrochemical processes in electrolysis devices and other electrochemical systems are highly complex and often difficult to disentangle. This lack of knowledge hinders identification and evaluation of physicochemical processes, limits understanding of how materials and device architecture influence performance, and ultimately inhibits principled design choices. There is a need for practical, robust procedures to gain physical insight into electrochemical device performance. This requires effective design of experiments to sufficiently characterize varying conditions within reasonable time constraints. In addition, new methods for coherent analysis of large electrochemical datasets are necessary to extract meaning from vast amounts of raw data.

Here, we introduce a novel approach to accelerate electrochemical characterization with standard instrumentation by utilizing rapid measurements in both the time and frequency domains. This “hybrid electrochemical impedance spectroscopy” (HEIS) method provides excellent resolution across a broad range of timescales while decreasing measurement time by more than an order of magnitude compared to conventional electrochemical impedance spectroscopy. The technique can be applied to quickly construct detailed electrochemical maps of energy conversion devices across multiple measurement condition dimensions, revealing physicochemical relationships that are hidden in sparse conventional datasets. We use this new approach to comprehensively characterize a commercial Li-ion battery as a function of C-rate and state-of-charge during charging/discharging and a reversible protonic ceramic electrolyzer/fuel cell (PCEC) under different temperature, atmosphere, and bias conditions. Altogether, more than 20,000 distinct HEIS experiments are performed, resulting in a rich dataset that can be assembled to form relaxation hypersurfaces - multi-dimensional analogs of the one-dimensional distribution of relaxation times (DRT) - which are then processed with new analysis techniques to reveal the underlying processes that govern device performance. This approach describes the electrochemical behavior of both batteries and PCECs with an unprecedented level of detail made possible by accelerated measurement and scalable data processing strategies. However, this study represents just one possible form of the mapping concept. The underlying principles and techniques can be adapted to develop various new ways to examine a wide variety of energy-conversion devices. The conceptual approach demonstrated here has the potential to reduce the time required to understand electrochemical materials and device architectures, enabling a faster design cycle.