3,364 research outputs found

    Advanced continuously variable transmissions for electric and hybrid vehicles

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    A brief survey of past and present continuously variable transmissions (CVT) which are potentially suitable for application with electric and hybrid vehicles is presented. Discussion of general transmission requirements and benefits attainable with a CVT for electric vehicle use is given. The arrangement and function of several specific CVT concepts are cited along with their current development status. Lastly, the results of preliminary design studies conducted under a NASA contract for DOE on four CVT concepts for use in advanced electric vehicles are reviewed

    Traction and Launch Control for a Rear-Wheel-Drive Parallel-Series Plug-In Hybrid Electric Vehicle

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    Hybrid vehicles are becoming the future of automobiles leading into the all-electric generation of vehicles. Electric vehicles come with a great increase in torque at lower RPM resulting in the issue of transferring this torque to the ground effectively. In this thesis, a method is presented for limiting wheel slip and targeting the ideal slip ratio for dry asphalt and low friction surfaces at every given time step. A launch control system is developed to further reduce wheel slip on initial acceleration from standstill furthering acceleration rates to sixty miles per hour. A MATLAB Simulink model was built of the powertrain as well as a six degree of freedom vehicle model that has been validated with real testing data from the car. This model was utilized to provide a reliable platform for optimizing control strategies without having to have access to the physical vehicle, thus reducing physical testing. A nine percent increase has been achieved by utilizing traction control and launch control for initial vehicle movement to sixty miles per hour

    Preliminary power train design for a state-of-the-art electric vehicle

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    Power train designs which can be implemented within the current state-of-the-art were identified by means of a review of existing electric vehicles and suitable off-the-shelf components. The affect of various motor/transmission combinations on vehicle range over the SAE J227a schedule D cycle was evaluated. The selected, state-of-the-art power train employs a dc series wound motor, SCR controller, variable speed transmission, regenerative braking, drum brakes and radial ply tires. Vehicle range over the SAE cycle can be extended by approximately 20% by the further development of separately excited, shunt wound DC motors and electrical controllers. Approaches which could improve overall power train efficiency, such as AC motor systems, are identified. However, future emphasis should remain on batteries, tires and lightweight structures if substantial range improvements are to be achieved

    Analysis of different powertrain configurations for a formula style electric racecar

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    The aim of this thesis is to provide useful framework for the design of the upcoming electric race car of Lund Formula Student Team. The thesis intends to find the different powertrain concepts on the state of the art. From the configurations, the thesis should provide outcomes of the performance, efficiency, complexity design and cost. Furthermore, the best concept should be find and a simple preliminary design is made.To compare the different concepts developed, a Matlab code was used, which simulates the vehicle dynamics of the race cars. A Simulink model wasbe used to analyse the different electric systems and come up with the most efficient solution. The results of the thesis show that the powertrain configuration that should perform better in a real competition is the design with four motors actuating one in each wheel. The reason behind it, is the abilty of the system to provide different torque at each wheel, known as torque vectoring. By distributing different torque at each wheel the race car is able to create a yaw movement to the body, allowing it to make turns at a higher velocity. The design shows the different parts composing the powertrain, and how each of the parts was chosen. To conclude the thesis, the four motor’s configuration is compared to the LFS20 design in order to explain how this powertrain improves the car results in the overall competitionOutgoin

    Multi-objective evolutionary design of an electric vehicle chassis

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    An iterative algorithm is proposed for determining the optimal chassis design of an electric vehicle, given a path and a reference time. The proposed algorithm balances the capacity of the battery pack and the dynamic properties of the chassis, seeking to optimize the tradeoff between the mass of the vehicle, its energy consumption, and the travel time. The design variables of the chassis include geometrical and inertial values, as well as the characteristics of the powertrain. The optimization is constrained by the slopes, curves, grip, and posted speeds of the different sections of the track. Particular service constraints are also considered, such as limiting accelerations due to passenger comfort or cargo safety. This methodology is applicable to any vehicle whose route and travel time are known in advance, such as delivery vehicles, buses, and race cars, and has been validated using telemetry data from an internal combustion rear-wheel drive race car designed for hill climb competitions. The implementation of the proposed methodology allows to reduce the weight of the battery pack by up to 20%, compared to traditional design methods

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

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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.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2
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