Optimal eco-driving in electric vehicles (EVs) can be challenging due to volatile, bidi-
rectional energy flows and the difficulty of directly sensing energy flows. The present
research investigates energy-related situation awareness (Energy Dynamics Aware-
ness, EDA) as a pilot study. EDA is a theoretical concept that helps to describe and
understand how visual energy feedback displays inform energy-efficient vehicle con-
trol decisions. We compared three methods (estimation tasks, subjective EDA rating
scale, and gaze behavior metric) to assess EDA under two different workload condi-
tions, using a video-based online study displaying EV driving scenes (N = 29). We
developed a novel approach to collect gaze behavior indicators using self-controlled
(i.e., manually directed) occlusion through keyboard input. Participants were asked to
estimate and compare the energy consumed in EV driving scenes while performing a
parallel visuospatial n-back task to induce cognitive load. Based on our findings, the
n-back task successfully induced cognitive load and self-directed occlusion showed
to be a promising method for energy display evaluation studies. The performance
of the consumption estimation task and display fixations were influenced by cogni-
tive workload, which has important implications for ecodriving interface design. As
the subjective and performance-related measures of EDA did not correlate, the results
contribute to the discussion on the divergence between subjective and objective mea-
sures of situation awareness. This pilot study encourages further research with a larger
sample and adapted methods.