Control in multi-motor electric vehicle with a FPGA platform

A new FPGA based platform is presented for controlling a Multi-Motor Electric Vehicle (EV). By exploring the FPGA parallel processing capabilities, two induction motor controllers, based on Field Orientation Control and Space Vector Modulation techniques, were merged in a single and compact chip. Implementation issues related with the limited number of dedicated multipliers were overcome using an efficient computational block, based on resource sharing strategy. The developed IP Cores were carefully optimized to fit in a low cost XC3S1000. Experimental results, obtained with a multi-motor EV prototype, demonstrate the proper operation of the proposed propulsion system

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

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

Design, development and characterisation of a FPGA platform for multi-motor electric vehicle control

Two three-phase squirrel-cage induction motors are used as a propulsion system of an electric vehicle (EV). A simple XC3S1000 FPGA is used to simultaneously control both electric motors, with field oriented control and space vector modulation techniques. To electronically distribute the torque between the two electric motors, a simple, yet effective, strategy based on a uniform torque distribution has been implemented. Experimental results obtained with a multi-motor EV prototype demonstrate the proper operation of the proposed system

FPGA Based Powertrain Control for Electric Vehicles

In this article an FPGA based solution for the advance control of multi-motor EVs was proposed. The design was build around a powertrain IP Core library containing the most relevant functions for the EV operation: motor torque and flux regulation, energy loss minimization and vehicle safety. Due to the parallel, modularity and reconfigurability features of FPGAs, this library can be reused in the development of several control architectures that best suits the EV powertrain configuration (single or multi-motor) and functional requirements. As proof of concept, the powertrain library was employed in the design of minimal control system for a bi-motor EV prototype and implemented in a low cost Xilinx Spartan 3 FPGA. Experimental verification of the control unit was provided, showing reasonable consumption metrics and illustrating the energy benefits from regenerative braking

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

A holistic DC link architecture design method for multiphase Integrated Modular Motor Drives

This article describes a holistic DC link architecture design method that considers the end-application of the drive and its corresponding constraints e.g. the maximum battery ripple current for a battery-supplied inverter. Also, the levers that are available to comply with the design criteria are presented e.g. the use of interleaved carrier waves. This holistic approach will result in a feasible and performant Integrated Modular Motor Drive from an application point of view. Finally, a platform is presented that was developed for feasibility and performance assessment of various DC link architectures obtained from the holistic design approach. The platform comprises a fifteen phase integrable modular motor drive for an Axial Flux Permanent Magnet Synchronous Machine. It allows non-intrusive reconfiguration of the DC link architecture and implementation of various control strategies and interleaved PWM schemes

High performance control of a multiple-DOF motion platform for driver seat vibration test in laboratory

Dynamic testing plays an important part in the vehicle seat suspension study. However, a large amount of research work on vibration control of vehicle seat suspension to date has been limited to simulations because the use of a full-size vehicle to test the device is an expensive and dangerous task. In order to decrease the product development time and cost as well as to improve the design quality, in this research, a vibration generation platform is developed for simulating the road induced vehicle vibration in laboratory. Different from existing driving simulation platforms, this research focuses on the vehicle chassis vibration simulation and the control of motion platform to make sure the platform can more accurately generate the actual vehicle vibration movement. A seven degree-of-freedom (DOF) full-vehicle model with varying road inputs is used to simulate the real vehicle vibration. Moreover, because the output vibration data of the vehicle model is all about the absolute heave, pitch and roll velocities of the sprung mass, in order to simulate the vibration in all dimensions, a Stewart multiple-DOF motion platform is designed to generate the required vibration. As a result, the whole vibration simulator becomes a hardware-in-the-loop (HIL) system. The hardware consists of a computer used to calculate the required vibration signals, a Stewart platform used to generate the real movement, and a controller used to control the movement of the platform and implemented by a National Instruments (NI) CompactRIO board. The data, which is from the vehicle model, can be converted into the length of the six legs of the Stewart platform. Therefore, the platform can transfer into the same posture as the real vehicle chassis at that moment. The success of the developed platform is demonstrated by HIL experiments of actuators. As there are six actuators installed in the motion platform, the signals from six encoders are used as the feedback signals for the control of the length of the actuators, and advanced control strategies are developed to control the movement of the platform to make sure the platform can accurately generate the required motion even in heavy load situations. Theoretical study is conducted on how to generate the reasonable vibration signals suitable for vehicle seat vibration tests in different situations and how to develop advanced control strategies for accurate control of the motion platform. Both simulation and experimental studies are conducted to validate the proposed approaches

Traction control for hybrid electric vehicles

Tese de mestrado integrado. Engenharia Elecrtotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Modeling and Design of Digital Electronic Systems

The paper is concerned with the modern methodologies for holistic modeling of electronic systems enabling system-on-chip design. The method deals with the functional modeling of complete electronic systems using the behavioral features of Hardware Description Languages or high level languages then targeting programmable devices - mainly Field Programmable Gate Arrays (FPGAs) - for the rapid prototyping of digital electronic controllers. This approach offers major advantages such as: a unique modeling and evaluation environment for complete power systems, the same environment is used for the rapid prototyping of the digital controller, fast design development, short time to market, a CAD platform independent model, reusability of the model/design, generation of valuable IP, high level hardware/software partitioning of the design is enabled, Concurrent Engineering basic rules (unique EDA environment and common design database) are fulfilled. The recent evolution of such design methodologies is marked through references to case studies of electronic system modeling,simulation, controller design and implementation. Pointers for future trends / evolution of electronic design strategies and tools are given