Supporting energy-efficient driving is essential for sustainable mobility in electric vehicles (EVs), where operational eco-driving (or ecodriving) can significantly reduce energy consumption. This study investigates the effects of two types of ecodriving displays—an Instantaneous Consumption Display (ICD) and an Optimal Speed Display (OSD)—on energy consumption and energy-related situation awareness (Energy Dynamics Awareness, EnDynA). In the EcoSimLab EV simulator, N = 94 participants were assigned to the ICD, OSD, or a control condition and completed multiple driving trials with varying situation complexity. Improvement, defined as the difference between the display and baseline conditions, in EnDynA and eco-driving performance, was greater in the display groups than in the control. The OSD, providing predictive speed recommendations by dynamic programming, was particularly effective in more complex driving scenarios. The ICD, offering real-time consumption feedback, facilitated learning in simpler scenarios. Findings underscore the importance of adaptive ecodriving support systems, balancing real-time feedback with predictive guidance.

Although interpretable controllers are widely used in vehicle systems, they have received limited attention as models of human driving behavior. This study explores whether two such control strategies, a preview-augmented Proportional-Integral-Derivative (PID) controller and a constraint-based Model Predictive Control (MPC) framework, can model human longitudinal driving behavior when adapted via Bayesian optimization. A structured dataset of human driving behavior, recorded with participants in a driving simulator, was used to train and evaluate both controllers across acceleration, deceleration, and cruising scenarios. MPC achieved lower overall deviation and more consistent performance across trials. These findings highlight the potential of combining interpretable control architectures with data-driven parameter adaptation to model human driving behavior effectively.

Driving simulators often lack fundamental components needed for accurate simulation of energy dynamics. We introduce EcoSimLab, a comprehensive electric vehicle driving simulation framework consisting of (1) a simulation of electric vehicle energy dynamics, (2) an optimization-based approach of structuring eco-driving behaviors, (3) a synthetic driver module as versatile benchmark model to analyze human behavior. Guided by fundamentals of energy modeling and considerations on human action regulation, we further present the development of the EcoDrivingTestPark, an exemplary set of energy-relevant scenarios to enable the analysis of individual differences in eco-driving and intervention effects (e.g., HMIs). To generate a first characterization of driving behavior, we conducted two empirical studies with human ( <?TeX $N_\text{S1}~=~31$?> Math 1 , <?TeX $N_\text{S2a}~=~41$?> Math 2 ) and synthetic drivers ( <?TeX $N_\text{S2b}~=~3$?> Math 3 ). Results indicate substantial variations in driver behavior and considerable challenges for human drivers to achieve synthetic driver performance. Implications for augmenting human action regulation in eco-driving are discussed.