EcoSimLab

"The vision of the IMIS driving simulator is an integrated driving energy simulation in which energy interfaces can be developed and evaluated and user-energy interaction issues can be investigated. Our aim is therefore the realistic simulation of energy-relevant parameters in driver-vehicle interaction"

Prof. Dr. Thomas Franke

Hardware

The EcoSimLab driving simulator features a 180° field of view using three 55-inch diagonal 120Hz displays, delivering stable 50fps rendering at 12k resolution. Powered by a Ryzen Threadripper 3970X and a GeForce RTX 4090, it provides reliable performance.

The steering wheel is adjusted in height and length by the driver, supporting the full 920° rotation of the Renault ZOE. Precise input resolution and above-spec force feedback are provided by a Fanatec ClubSport DD+ Module. The pedals are adjusted in pedal-travel and resistance force, with a load cell used for the brake pedal sensor to offer improved braking characteristics.

The simrig is custom-built to match the Renault ZOE's interior measurements and ergonomics, ensuring that participants experience driving conditions as close to the real vehicle as possible. This accurate ergonomic replication is crucial, as it allows participants to perform complex maneuvers without added difficulty or simplification, supporting realistic driver behavior during studies. Integrated OEM parts and interfaces further enhance the authenticity of the simulation.

Additionally, EcoSimHub includes a study operator workspace, a fully remote setup for monitoring and controlling scenarios during studies. This workspace enables a single operator to prepare the simulator and manage the study, ensuring efficient study execution.

Software

BeamNG

At EcoSimLab, we leverage BeamNG as our driving simulation software. Built on a highly accurate physics engine, BeamNG offers detailed, high-fidelity rendering. Its flexible platform allows us to customize vehicles or create new ones entirely, while the powerful API supports seamless integration with our research tools.

EcoInterfaces

A core component of our research at EcoSimLab is EcoInterfaces, focusing on human-energy interaction. These interfaces are designed to enhance driver awareness and energy efficiency. Cross-platform compatibility allows for their use not only in simulators but also for testing in real vehicles, bridging the gap between virtual and physical environments.

Research Vehicle

The Renault ZOE Q90 serves as the reference vehicle for the AMORi project. Its reverse-engineered powertrain is precisely modeled in BeamNG, with accurate loss and efficiency representation. Fine-tuned controls, exact pedal mapping, and a customized chassis replicate real-world aerodynamics and road behavior. Our open-source simulation is so precise, it can be used as an objective metric in efficiency studies.

EcoDriving Testpark

The EcoDriving Testpark addresses a key challenge in driving efficiency studies: typical drives often lack the frequency of energy-relevant maneuvers, wasting valuable study time on steady-speed driving. Implemented as a custom map and scenario in BeamNG, the Testpark includes as many energy-relevant maneuvers as possible with minimal filler distances. Designed to differentiate driver characteristics, it features a balanced spread of control complexity. The project is open-source and continuously refined to support advanced research in eco-driving.

Study Control App

The Study Control App transforms BeamNG into a robust platform for user studies. With a built-in scenario editor, it enables precise study setup and remote monitoring. The app’s backend connects seamlessly with databases, MATLAB, EcoInterfaces, and eyetracking systems, making it a versatile tool for data collection and analysis. Open APIs ensure flexibility and integration with various research modules.

EcoMPC

EcoMPC is an economic model predictive controller that powers our EcoInterfaces. Built on an offline algorithm, it calculates the most efficient driving behavior for predefined conditions and routes. By comparing the current driving state with the optimal one, it provides impulses for corrective action. With a dynamic prediction horizon and real-time operation through linearization, this MATLAB-based system ensures efficient and adaptive driving control.

Infrastructure

Our infrastructure is designed with flexibility in mind, enabling bidirectional communication between the simulation and additional modules like EcoInterfaces and EcoMPC. At its core, it integrates BeamNG.tech, the driving data database, and the Study Control App. This robust setup allows for the creation and execution of large-scale driving simulator studies, supporting high participant counts. With precise data collection, real-time monitoring, and seamless integration with tools such as MATLAB, our infrastructure ensures that both new and ongoing studies can be conducted efficiently and effectively.

Research

Theses

Jonas Busch (2023). Entwicklung und Anwendung eines Auswertungstools für Blickbewegungsdaten in Fahrsimulationsstudien mit Live-Energieanzeigen.

Tobias Harms (2023). Integration eines MATLAB Eco-MPC in die Fahrsimulationssoftware BeamNG.tech im Kontext der nutzerzentrierten Energieeffizienzanzeigen Forschung in der Elektromobilität.

Tobias Reins (2023). Entwicklung eines Systems zur Planung, Durchführung und Auswertung von Simulator-Studien.

Josua Potschien (2022). Systematische Untersuchung von Metriken hinsichtlich der Bewertung der Energieeffizienz und Eignung in verschiedenen Energieanzeigen.

Indrani Breier (2021). Tipp-Basiertes Ecodriving Feedback: Entwicklung eines Post-Manöver-Assistenzsystems zum Training energieeffizienten Fahrverhaltens in batteriebetriebenen Elektrofahrzeugen.

Anton de Vries (2021). Beschleunigungsdisplays im Fahrsimulator - Untersuchung des Einflusses der Displayart auf die Energieeffizienz.

Lukas Bernhardt (2019). Construction, Realization and Validation of a Low-Cost Driving-Simulator and a Renault ZOE Data Interface.

Pascal Stieglitz (2018). Ausbau der Software BeamNG zu einer Fahrsimulationsumgebung für Untersuchungen in der Nutzer-Energie-Interaktion.

Publications

Gödker, M., Schmees, S., Bernhardt. L., Heidinger, J., Görges, D., and Franke, T. (2024). Driving Simulation for Energy Efficiency Studies: Analyzing Electric Vehicle Eco-Driving With EcoSimLab and the EcoDrivingTestPark. In Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '24). Association for Computing Machinery, New York, NY, USA, 32–42. https://doi.org/10.1145/3640792.3675706.

Gödker, M., & Franke, T. (2024). Assessing Energy-Related Situation Awareness Using Self-Controlled Occlusion During Electric Vehicle Driving Scenes. 148, 286-296. https://doi.org/10.54941/ahfe1005219.

Heidinger, J., Bernhardt, L., & Franke, T. (2023). Ecosimlab - A Low-Cost Driving Simulator Environment for Examining Human Factors in Vehicle Energy Efficiency. AutomotiveUI '23 Adjunct: Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, , Ingolstadt (Germany), 18.-21.09.2023, 66-70. https://doi.org/10.1145/3581961.3609881.

Moll, V. E., & Franke, T. (2021). Biased energy efficiency perception based on instantaneous consumption displays–Indication for heuristic energy information processing. Applied Ergonomics, 94, 103399. https://doi.org/10.1016/j.apergo.2021.103399.

Franke, T., Görges, D., & Arend, M. G. (2019, September). The Energy Interface Challenge. Towards Designing Effective Energy Efficiency Interfaces for Electric Vehicles. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 35-48). https://doi.org/10.1145/3342197.3344526.

Bernhardt, L., Moll, V., & Franke, T. (2019). Entwicklung und Validierung einer Fahrsimulationsumgebung für Interface-Testing im Kontext Elektromobilität. 3. Kongress der Fachgruppe Verkehrspsychologie, Saarbrücken, 05.-07.03.2019. https://doi.org/10.13140/RG.2.2.26178.32962/1.