Evaluation of the driving performance and user acceptance of a predictive eco-driving assistance system for electric vehicles

Chada, Sai Krishna; Görges, Daniel; Ebert, Achim; Teutsch, Roman; Subramanya, S. · 2023 · OpenAlex-citations

DOI: 10.1016/j.trc.2023.104193

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Summary

This study addresses the challenge of improving energy efficiency in battery electric vehicles (BEVs) while maintaining driving safety and comfort. Motivated by the limited range of BEVs and the need for sustainable mobility solutions, the authors developed and evaluated a predictive eco-driving assistance system (pEDAS). The system aims to assist drivers in adopting energy-efficient behaviors by providing continuous, context-aware speed recommendations that account for surrounding traffic, traffic light signals, and road topology. The pEDAS utilizes a two-level model predictive control (MPC) framework comprising a reference-tracking MPC and a car-following MPC. The system integrates data from onboard sensors, signal phase and timing (SPaT) messages from traffic infrastructure, and geographical route information. A switching logic determines which controller is active based on the distance to preceding vehicles; the reference-tracking MPC engages in freeway scenarios to track optimal speeds derived from dynamic programming or green-wave algorithms, while the car-following MPC activates when preceding vehicles are within sensor range. The system provides visual feedback for optimal speed suggestions and auditory alerts for unsafe car-following situations. To evaluate the system, the researchers conducted user studies with 41 participants using a dynamic driving simulator, testing the pEDAS in both urban and highway environments. Objective analysis of the driving performance revealed that drivers using pEDAS achieved mean energy savings of up to 10%. The system also helped reduce speed limit violations and enabled drivers to avoid unnecessary stops at signalized intersections by adhering to green-wave optimal speeds. Subjective evaluation of user acceptance was conducted using the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). The results indicated an overall positive attitude toward the system. Specifically, perceived usefulness and perceived behavioral control were identified as significant factors influencing the participants' behavioral intention to use the pEDAS. The significance of this work lies in its comprehensive evaluation of a continuous predictive eco-driving assistance system for BEVs, addressing a gap in literature where such systems were previously evaluated only for internal combustion engine vehicles or in simplified scenarios. By demonstrating both objective energy savings and high user acceptance, the study highlights the potential of integrating advanced control strategies with human-machine interfaces to enhance the efficiency and adoption of electric vehicles. The findings suggest that providing clear, continuous, and context-specific feedback can effectively modify driver behavior to reduce energy consumption without compromising safety.

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Topics

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• eco driving
• in vehicle coaching
• acceptance adoption

Information type

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• Methodological Resource: tool software
• Theoretical Contribution: computational model