Race-track testing of a torque vectoring algorithm on a motor-in-wheel car using a model-based methodology with a HiL and multibody simulator setup

Dendaluce, Martin; Iglesias, I; Martín, Adrián; Prieto, Pablo; Peña, Alberto

doi:10.1109/itsc.2016.7795958

Cited by 15 publications

(13 citation statements)

References 8 publications

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“…In what respects to the setup and approach for the training and validation of these NNs, we generated a broad and diverse dataset, aiming to avoid overfitting and ensure good generalization, including a variety of road/track models (Nürburgring, Inta and different generic test tracks) and different driving styles (normal, aggressive, borderline and drifting) by different drivers. For this we relied on a high-fidelity multibody vehicle dynamics simulator (Dynacar, [84,85], also involved in real circuit tests in previous work for validation purposes [54,84]) integrated into an elaborate model-based development framework (using MatLab-Simulink for accelerated MiL tests as well as HiL validation [6,86]), together with models of other relevant components, subsystems and control units. Therefore, validation tests were performed using data generated by the same setup but driving differently.…”

Section: Resultsmentioning

confidence: 99%

“…This can be very conveniently exploited by reassigning a part of the torque from the interior wheels to the exterior ones. As has also already assessed by means of race-track tests in previous works [6,39,54,55], this does not only avoid the inner wheel to spin because of the lower traction capacity but it also generates an additional yaw moment, helping the vehicle rotate over its vertical axis towards the direction of the curve, thus mitigating understeer-i.e., the front wheels turning the car less than expected according to their steering angle- [38,56].…”

Section: Targeted Application: Quadruple Electric-motor Vehiclementioning

confidence: 96%

“…Firstly focusing on the vehicle itself, quadruple-electric-motor powertrains present several advantages from the control point of view. By having independent control over the torque applied to each of the four wheels, they allow the implementation of sophisticated control strategies, basing on the Torque-Vectoring approach previously mentioned also in subsection 2.2 and Figure 2 [6,38,54]. In comparison to the hybrid topology illustrated in this figure, in the targeted vehicle there is no differential on the rear axis, thus, in the same way as on the front axis, the torque on each of the rear wheels can also be directly controlled.…”

Section: Targeted Application: Quadruple Electric-motor Vehiclementioning

confidence: 99%

See 2 more Smart Citations

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

et al. 2019

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The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.

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Section: Resultsmentioning

confidence: 99%

Section: Targeted Application: Quadruple Electric-motor Vehiclementioning

confidence: 96%

Section: Targeted Application: Quadruple Electric-motor Vehiclementioning

confidence: 99%

See 1 more Smart Citation

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

et al. 2019

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“…El modelo de vehículo se implementa en Dynacar, que es una plataforma de simulación de dinámica de vehículos de alta fidelidad desarrollada por Tecnalia Research & Innovation [4]. Este software de simulación de vehículos ha sido validado a través de varias pruebas de pista [2].…”

Section: Simulador De Dinámica Vehicular -Dynacarunclassified

Evaluación de un algoritmo de torque vectoring con capacidad de frenado regenerativo

Parra¹,

Pérez²

2020

XL Jornadas De Automática: Libro De Actas (Ferrol, 4-6 De Septiembre De 2019)

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“…The vehicle model is implemented in Dynacar, which is a high fidelity vehicle dynamics simulation platform developed by Tecnalia Research & Innovation [36]. This vehicle simulation software has been validated through several racetrack tests [37,38] and it can be used in a model-in-the-loop framework to test the performance of different automotive aimed control systems.…”

Section: Dynacar High Fidelity Dynamicsmentioning

confidence: 99%

Intelligent Torque Vectoring Approach for Electric Vehicles with Per‐Wheel Motors

2018

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Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.

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Race-track testing of a torque vectoring algorithm on a motor-in-wheel car using a model-based methodology with a HiL and multibody simulator setup

Cited by 15 publications

References 8 publications

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

Evaluación de un algoritmo de torque vectoring con capacidad de frenado regenerativo

Intelligent Torque Vectoring Approach for Electric Vehicles with Per‐Wheel Motors

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