Development of a Driver-in-the-Loop Simulation to Evaluate the Performance to Energy Trade-Off of Active Dynamics Systems on an Electric Race Car

Chrysakis, Georgios; Vogel, Jonathan; Nikzadfar, Kamyar · 2022 · Crossref

DOI: 10.4271/2022-01-5040

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Summary

This study addresses the critical trade-off between vehicle performance and energy consumption in electric race cars, specifically within the context of Formula Student competitions. As electrification becomes central to motorsport, battery limitations necessitate a balance between maximum range and dynamic capability. The research evaluates four active dynamics control systems—Automatic Rear Steering (ARS), Drag Reduction System (DRS), Semi-Active Suspension (SAS), and Torque Vectoring (TV)—to determine their individual and combined effects on handling, power usage, and driver workload. The authors identify a gap in existing literature regarding the simultaneous evaluation of these systems’ energy costs and their interaction with human drivers, rather than relying solely on objective simulation metrics or subjective feedback. To investigate this, the authors developed a Driver-in-the-Loop (DiL) simulation using the Cruden Panthera software suite and a customized multibody Simulink model of the Coventry University PR87E electric race car. The vehicle model was expanded to include an electric drivetrain with four independent motors, a battery model, and specific suspension geometry. The simulation was validated against logged data from a comparable Formula Student vehicle, showing realistic responses despite minor discrepancies in oversteer events. Testing involved both driverless closed-loop maneuvers (constant radius skidpad) to baseline performance and DiL tests (skidpad, double lane change, slalom, and autocross) to assess drivability. Driver workload was quantified using the Emergency Avoidance Performance Index (EAPI), while performance was measured via lateral acceleration and lap times, and efficiency via battery power draw. The results indicate that Torque Vectoring (TV) provided the greatest improvement in vehicle performance but incurred the highest energy cost. Automatic Rear Steering (ARS) offered moderate performance gains but significantly improved drivability, particularly when configured for sideslip control, which reduced vehicle sideslip angles and stabilized driver inputs. Semi-Active Suspension (SAS) enhanced steady-state cornering performance by reducing tire load variation but had minimal impact on transient maneuvers, making its energy cost difficult to justify. DRS improved straight-line efficiency by reducing aerodynamic drag but compromised cornering performance. Crucially, the combination of TV, DRS, and ARS was identified as the optimal configuration. This setup quantifiably improved lap times and driver workload while reducing total power consumption compared to the baseline vehicle, as DRS allowed the other systems to operate more efficiently by managing aerodynamic loads. The study concludes that integrating multiple active dynamics systems can yield synergistic benefits that outweigh their individual energy penalties. By optimizing the interaction between torque vectoring, rear steering, and aerodynamic drag reduction, electric race cars can achieve superior handling and efficiency simultaneously. This approach provides a framework for motorsport teams and automotive manufacturers to design electric vehicles that maximize performance without exceeding strict energy constraints, highlighting the importance of considering driver-in-the-loop dynamics in the development of active control systems.

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• steering pattern

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