Design and Implementation of a Torque Vectoring Model for Enhanced Stability in Formula Student Vehicles

Abstract

This paper presents the design and implementation of a torque vectoring control strategy aimed at improving the dynamic stability and handling performance of a Formula Student electric vehicle. The approach dynamically distributes torque between individual wheels based on real-time vehicle parameters such as speed, yaw rate, lateral acceleration, steering input, throttle position, and track width. A comprehensive algorithm is developed to compute dynamic load distribution using the vehicle\'s mass, center of gravity height, and lateral dynamics. Yaw correction is applied to counteract instability during aggressive cornering, ensuring enhanced responsiveness and precise directional control. The torque output is constrained within safety thresholds to prevent motor overload and maintain operational stability. The proposed system is implemented using MATLAB/Simulink with the Powertrain Blockset, enabling a modular and real-time simulation environment. Simulation results demonstrate the effectiveness of the control logic in improving vehicle stability, minimizing yaw deviation, and maintaining control in potential off-track conditions, offering a viable solution for high-performance and safety-critical Formula Student applications.

Introduction

Torque vectoring is an advanced vehicle control strategy that dynamically distributes torque to individual wheels, enhancing stability, handling, cornering, braking, and energy efficiency. A yaw rate-based approach continuously monitors deviations between actual and desired yaw rates, adjusting torque in real time. Integrating tire–road interaction modeling—which accounts for tire compound, tread, road texture, load, and wear—enables precise torque allocation based on traction and rolling resistance variations. This is particularly important during braking or cornering, where dynamic load transfer affects tire grip.

Simulation and Implementation:
The system is implemented in MATLAB/Simulink with the Powertrain Blockset for high-fidelity, modular simulations. Key subsystems include:

• Driver Inputs: Throttle, braking, and steering simulation.

• Torque Vectoring Controller: Computes differential torque for each motor.

• Powertrain: Simulates electric motors, controllers, and drivetrain losses.

• Vehicle Dynamics: Models longitudinal/lateral behavior, yaw, and tire-road interaction.

• Feedback/Monitoring: Tracks yaw rate, slip angle, lateral acceleration, and controller performance.

The modular design allows scalability (e.g., from 2-motor to 4-motor setups) and future integration with hardware-in-the-loop testing.

Control Algorithm:
The torque vectoring algorithm is rule-based, using real-time inputs:

• Vehicle speed, yaw rate, lateral acceleration, steering angle, throttle, track width, vehicle mass, and CG height.
  It calculates:

• Lateral load transfer for grip prioritization.

• Yaw correction torque to maintain directional stability.

• Final torque commands for left/right wheels to optimize handling, braking, and regenerative energy recovery.

Applications:

• Enhances cornering, braking, and overall vehicle stability.

• Balances energy efficiency via regenerative braking.

• Suitable for high-performance electric vehicles, Formula Student EVs, and autonomous platforms.

Literature Insights:

• Torque vectoring improves AWD EV handling, stability, and acceleration (Giacomini et al., Jneid et al.).

• Nonlinear predictive and feedforward control strategies enhance vehicle maneuverability and energy efficiency (Svec et al., Castellanos Molina et al.).

• Simplified and modular implementations allow integration with existing braking systems while maintaining safety and flexibility (Pugi et al., Çelik et al., Sayssouk et al.).

Conclusion:
Yaw rate-based torque vectoring, combined with tire–road modeling and modular simulation architecture, provides a robust, real-time solution to optimize vehicle dynamics, safety, and energy efficiency in electric vehicles.

Conclusion

This research presents the design, implementation, and evaluation of a torque vectoring control strategy tailored for a Formula Student electric vehicle, developed and validated using the MATLAB/Simulink environment. The proposed control algorithm dynamically distributes torque between the driven wheels based on real-time vehicle states, including steering input, yaw rate, lateral acceleration, and load distribution. Simulation results demonstrate that the torque vectoring system significantly improves yaw stability, steering responsiveness, and overall handling performance. Compared to a baseline configuration with no differential torque control, the vehicle equipped with torque vectoring exhibited: 1) Enhanced yaw rate tracking and reduced deviation from the desired path 2) Improved lateral load balance and reduced slip in transient maneuvers 3) Effective recovery from off-track conditions through active torque correction By integrating the control algorithm within a modular Simulink model built upon the Powertrain Blockset, the study also highlights the practical feasibility of deploying such systems in simulation environments reflective of Formula Student race conditions.

References

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Copyright

Copyright © 2025 Prof. Dr. Laxmikant Mangate, Shreyas D Khamkar, Ayush A. Kude, Adwait Kharmale , Shefali S. Lohakare , Aditya R. Madale . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.