Analysis of power converters with devices of SiC for applications in electric traction systems

This article presents the analysis of two topologies of power converters. Voltage Source Inverter (VSI) and Current Source Inverter (CSI) proposals for traction system applications, these topologies are implemented with silicon carbide devices. The use of SiC semiconductors allow working at high switching frequency (100KHz), increase the working temperature range and decreasing power losses during conduction and activation of the semiconductors. The objective is analyze these topologies and select the one that provides the best performance and behavior at high frequency to improve it on a electric traction system.Postprint (author's final draft

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

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

Traction control in electric vehicles

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores. Área de Especialização de Automação. Faculdade de Engenharia. Universidade do Porto. 201

Implementation of Algorithm for Vehicle Anti-Collision Alert System in FPGA

Vehicle safety has becoming one of the important issues nowadays, due to the fact the number of road accidents, which cause injuries, deaths and also damages, keeps on increasing. One of the main factors which contribute to these accidents are human's lack of awareness and also carelessness. This paper presents the development and implementation of an algorithm to be utilized for vehicle anti-collision alert system, which may be useful to reduce the occurrence of accidents. This algorithm, which is to be deployed with the front sensors of the vehicle, is capable of alerting any occurrence of sudden slowing or static vehicles ahead, by sensing the rate of distance change. Furthermore, it also triggers an alert if the driver is breaching the safe distance from the vehicle ahead. This algorithm has been successfully implemented in Altera DE0 FPGA and its functionality was validated via hardware experimental tests

On-line Junction Temperature Estimation of SiC Power MOSFETs

L'abstract è presente nell'allegato / the abstract is in the attachmen

Traction control for hybrid electric vehicles

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

INVESTIGATION OF RULE-BASED CONTROL STRATEGY IN A SERIES-PARALLEL HYBRID ELECTRIC VEHICLE

Advance in technology has lead many bad effects especially to our nature. In automotive industry, vehicle that use internal combustion engine has being a major need to the people. Internal Combustion Engine has cause major air pollution because of dangerous gaseous such as carbon dioxide, sulphur monoxide, and sulphur dioxide that can cause a lot of disease. Hybrid technology has come as a solution to reduce the emission of this dangerous gas by reducing the usage of internal combustion engine. The idea of hybrid technology is combining the internal combustion engine with electric motor in order to achieve this target. The purpose of this project is to develop the best control strategy to minimize the usage of energy called energy management. Many method will be test to discover the best strategy to minimize the energy usage in hybrid electric vehicle. All type of control strategy will be demonstrated in the Simulink Matlab software. The types of electric motor use are permanent magnet synchronous motor