Understanding the knowledge gaps in ecodriving: analysis of knowledge accuracy, uncertainty, and action regulation

Abstract Supporting ecodriving in battery-electric vehicles (BEVs) requires feedback aligned with drivers’ mental representations, as effective regulation depends on know-how and know-why. When mental representations are inadequate or confidence exceeds actual knowledge, this can undermine performance and feedback processing. This study primarily examined drivers’ mental representations of ecodriving via thematic analysis, focusing on knowledge gaps (missing beliefs, situational references, reasoning depth) and references to input–comparator–output information. As a complementary and exploratory component, knowledge accuracy, uncertainty due to a lack of knowledge, driving behaviour, and performance across different feedback approaches are additionally analysed to contextualise and further specify the qualitative insights. In a driving simulator study, participants ( N = 63) drove under one of three conditions: no feedback (G1), real-time consumption trace (G2), or optimal speed recommendation (G3). Afterwards, they provided ecodriving tips and technical explanations, offering insights into their understanding. Qualitative analysis showed broad familiarity with general ecodriving principles (e.g., smooth driving) but little precise or technically grounded guidance. Misconceptions were common across all groups, especially on regenerative braking, acceleration, and pedal use. Exploratory quantitative comparisons suggested that G3 reported lower uncertainty and drove slower in constant-speed phases than G1. G2 used mechanical braking more and regenerative braking less than G3. We did not observe statistically reliable group differences in knowledge accuracy or mean energy consumption. Findings indicate that cognitively aligned feedback must go beyond prescribing speed selection or energy raw data. To foster robust ecodriving and reduce uncertainty, systems should support causal understanding and accurate, transferable mental representations.