Improvement of Lap-Time of a Rear Wheel Drive Electric Racing Vehicle by a Novel Motor Torque Control Strategy

Ghosh, Jyotishman; Tonoli, Andrea; Amati, Nicola

doi:10.4271/2017-01-0509

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Data indicate a slight difference in their lateral accelerations, meaning that both vehicles can traverse curves at similar speeds, whether the speeds are low or high. However, when it comes to reaching high speeds, the 4WD vehicle reaches the end of the straight faster than the 2WD vehicle [36][37][38]. As a result, the 4WD vehicle must start braking earlier to compensate for its higher top speed.…”

Section: Discussionmentioning

confidence: 99%

Performance Prediction of a 4WD High-Performance Electric Vehicle Using a Model-Based Torque-Vectoring Approach

Serralvo Neto,

Palermo,

Giacomini

et al. 2023

WEVJ

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Electric vehicles (EVs) enable the integration of powertrains with multiple motors, allowing for the adjustment of torque delivered to each wheel. This approach permits the implementation of torque vectoring techniques (TV) to enhance the vehicle’s stability and cornering response, providing better control of yaw moments. This study utilizes comprehensive telemetry data and an advanced simulator model to assess the influence of torque vectoring (TV) on a Formula SAE (Society of Automotive Engineers) competition vehicle. The telemetry data were collected from a fully instrumented 2WD car, which was then employed to calibrate the simulation model. The calibrated model was subsequently utilized to predict the performance enhancements that could be achieved by implementing a 4WD system. The methodology proved to be a valuable contribution to vehicle design development. This approach also helps evaluate the potential advantages of torque vectoring for drivers with limited experience.

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Section: Discussionmentioning

confidence: 99%

Performance Prediction of a 4WD High-Performance Electric Vehicle Using a Model-Based Torque-Vectoring Approach

Serralvo Neto,

Palermo,

Giacomini

et al. 2023

WEVJ

View full text Add to dashboard Cite

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“…Dead-band controllers, low-pass filters, or integral actuation can improve this problem. Table 2 summarizes the main publications on SMC controllers related to DYC [16][17][18][19][20][21][22][23][24][25].…”

Section: Ref(s)mentioning

confidence: 99%

Comparison of Typical Controllers for Direct Yaw Moment Control Applied on an Electric Race Car

2021

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Direct Yaw Moment Control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer and faster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, a more advanced comparison of the important characteristics of the controllers’ performance is lacking, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle, and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified Proportional Integral Derivative (PID) controller showed that its performance is comparable to other more complex control techniques such as Model Predictive Control (MPC).

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Neural Network Inverse Optimal Control of Ground Vehicles

Cespi

Gennaro

Castillo–Toledo

et al. 2023

Neural Process Lett

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In this paper an active controller for ground vehicles stability is presented. The objective of this controller is to force the vehicle to track a desired reference, ensuring safe driving conditions in the case of adhesion loss during hazardous maneuvers. To this aim, a nonlinear discrete-time inverse optimal control based on a neural network identification is designed, using a recurrent high order neural network (RHONN) trained by an Extended Kalman Filter. The RHONN ensures stability of the identification error, while the controller ensures the stability of the tracking errors. Moreover, a discrete-time reduced order state observer is utilized to reconstruct the lateral vehicle dynamic not usually available. For the control problem, the references of the lateral velocity and yaw rate are given by a dynamic system mimicking an ideal vehicle having not-decreasing tire lateral characteristics. The proposed approach avoids the identification of the Pacejka’s lateral parameters of the tires, so simplifying the input control determination. Moreover, an optimal control is proposed to optimize the actuator effort and power, usually bounded. Control gains are determined using optimal “nature-inspired" algorithms such as particle swarm optimization. Test maneuvers, performed through the full vehicle simulator CarSim®, have been used to test correctness, quality and performances of the observer, the neural identifier and the inverse optimal controller. Robustness of the reduced order discrete-time state observer is also discussed for different sample times. Finally, a fair comparison between optimal and non-optimal control schemes is presented, highlighting the numerical results obtained in simulation.

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Improvement of Lap-Time of a Rear Wheel Drive Electric Racing Vehicle by a Novel Motor Torque Control Strategy

Cited by 5 publications

References 32 publications

Performance Prediction of a 4WD High-Performance Electric Vehicle Using a Model-Based Torque-Vectoring Approach

Performance Prediction of a 4WD High-Performance Electric Vehicle Using a Model-Based Torque-Vectoring Approach

Comparison of Typical Controllers for Direct Yaw Moment Control Applied on an Electric Race Car

Neural Network Inverse Optimal Control of Ground Vehicles

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