3-Phase Brushless Permanent-Magnet Motor Control for Hybrid Electric Vehicle In-Wheel Motor

This report discusses the project done by the author on the proposed topic, which is 3-Phase Brushless Permanent-Magnet Motor Control for Hybrid Electric Vehicle In-Wheel Motor. The project is about applying a 3-phase brushless in-wheel motor (IWM) to drive the rear wheels of a conventional vehicle based on the hybrid electric vehicle technology. The hybrid electric vehicle achieves better fuel economy than a conventional vehicle

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

Estudo de modelagem de veículos elétricos e estratégia de controle de torque para sistemas de frenagens regenerativa e antitravamento

Orientador: José Antenor PomilioTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Os veículos elétricos têm despertado crescente interesse devido à sua capacidade para reduzir a poluição no meio ambiente, usando elementos de energia elétrica acumulado em baterias e supercapacitores para o acionamento da máquina elétrica no lugar de um motor de combustão interna. Por outro lado, a baixa autonomia do veículo elétrico continua sendo uma barreira para seu sucesso comercial. Instituções automobilísticas junto com a Academia enfrentam esse desafio com diversas soluções para aumentar a energia disponível. Entre as possibilidades está a frenagem regenerativa. A frenagem regenerativa é um processo no qual recupera-se energia de um veículo durante as desacelerações. Esta pesquisa se concentra nas frenagens para diversas condições com mudanças da superficie da estrada, considerando o sistema de frenagem regenerativo e o sistema de antibloqueio. Analisamos e revisamos os aspectos básicos da modelagem de um veículo com/sem ABS, assim como o comportamento dinâmico das rodas e mostramos uma contribuição para o estudo do controle de torque na máquina e estratégias de controle para o torque distribuído na combinação e cooperação entre o torque elétrico e o mecânico, mesmo com mudanças do solo e de métodos de operação, como descidas, obtendo estabilidade do veículo e recuperação de energiaAbstract: The interest in electric vehicles has grown worldwide due to their efficiency for reducing environmental pollution, by using energy elements such as batteries and supercapacitors to drive the electric machine, instead of an internal combustion engine. Contrarily, the low vehicle autonomy remains a barrier to their commercial success. Therefore, automotive institutions together with academics face the challenge through various solutions to increase the available energy. The regenerative braking is one of the implementations that helps a better use of the stored energy. Regenerative braking is a process in which energy is recovered from a vehicle during decelerations. This research focuses on braking for various road surface conditions. Furthermore, it considers the regenerative braking and the anti-lock braking systems regarding energy recovery performance for friction coefficient changes. In this work, we will review and analyze the basic aspects of the modeling of a vehicle with or without ABS, as well as the dynamic behavior of wheels. We will also present a contribution to the study of torque control and control strategies for the torque distribution regarding combination and co-operation between electric and mechanical torque. This process is done despite changes in ground surfaces and operating methods such as downhill, leading to better performance in the flexibility of vehicle stability and in the recovery of powerDoutoradoEnergia EletricaDoutora em Engenharia Elétrica149810/2013-0CAPESCNP

High-precision hydraulic pressure control based on linear pressure-drop modulation in valve critical equilibrium state

High precision and fast response are of great significance for hydraulic pressure control in automotive braking systems. In this paper, a novel sliding mode control based high-precision hydraulic pressure feedback modulation is proposed. Dynamical models of the hydraulic brake system including valve dynamics are established. An open loop load pressure control based on the linear relationship between the pressure-drop and coil current in valve critical open equilibrium state is proposed, and also experimentally validated on a hardware-in-the-loop test rig. The control characteristics under different input pressures and varied coil currents are investigated. Moreover, the sensitivity of the proposed modulation on valve's key structure parameters and environmental temperatures are explored with some unexpected drawbacks. In order to achieve better robustness and precision, a sliding mode control based closed loop scheme is developed for the linear pressure-drop modulation. Comparative tests between this method and the existing methods are carried out. The results validate the effectiveness and superior performance of the proposed closed loop modulation method

Interdisciplinary design methodology for systems of mechatronic systems focus on highly dynamic environmental applications

This paper discusses a series of research challenges in the design of systems of mechatronic systems. A focus is given to environmental mechatronic applications within the chain “Renewable energy production - Smart grids - Electric vehicles”. For the considered mechatronic systems, the main design targets are formulated, the relations to state and parameter estimation, disturbance observation and rejection as well as control algorithms are highlighted. Finally, the study introduces an interdisciplinary design approach based on the intersectoral transfer of knowledge and collaborative experimental activities

Low adhesion detection and identification in a railway vehicle system using traction motor behaviour

It is important to monitor the wheel-rail friction coefficient in railway vehicles to improve their traction and braking performance as well as to reduce the number of incidents caused by low friction. Model based fault detection and identification (FDI) methods, especially state observers have been commonly used in previous research to monitor the wheel-rail friction. However, the previous methods cannot provide an accurate value of the friction coefficient and few of them have been validated using experiments. A Kalman filter based estimator is proposed in this research project. The developed estimator uses signals from the traction motor and provides a new and more efficient approach to monitoring the condition of the wheel-rail contact condition. A 1/5 scaled test rig has been built to evaluate the developed method. This rig comprises 2 axle-hung induction motors driving both the wheelsets of the bogie through 2 pairs of spur gears. 2 DC generators are used to provide traction load to the rollers through timing pulleys. The motors are independently controlled by 2 inverters. Motor parameters such as voltage, current and speed are measured by the inverters. The speed of the wheel and roller and the output of the DC generator are measured by incremental encoders and Hall-effect current clamps. A LabVIEW code has been designed to process all the collected data and send control commands to the inverters. The communication between the PC and the inverters are realized using the Profibus (Process Field Bus) and the OPC (Object Linking and Embedding (OLE) for Process Control) protocol. 3 different estimators were first developed using computer simulations. Kalman filter and its two nonlinear developments: extended Kalman filter (EKF) and unscented Kalman filter (UKF) have been used in these 3 methods. The results show that the UKF based estimator can provide the best performance in this case. The requirement for measuring the roller speed and the traction load are also studied using the UKF. The results show that it is essential to measure the roller speed but the absence of the traction load measurement does not have significant impact on the estimation accuracy. A re-adhesion control algorithm, which reduces excessive creepage between the wheel and rail, is developed based on the UKF estimator. Accurate monitoring of the friction coefficient helps the traction motor work at its optimum point. As the largest creep force is generated, the braking and accelerating time and distance can be reduced to their minimum values. This controller can also avoid excessive creepage and hence potentially reduce the wear of the wheel and rail. The UKF based estimator development has been evaluated by experiments conducted on the roller rig. Three different friction conditions were tested: base condition without contamination, water contamination and oil contamination. The traction load was varied to cover a large range of creepage. The importance of measuring the roller speed and the traction load was also studied. The UKF based estimator was shown to provide reliable estimation in most of the tested conditions. The experiments also confirm that it is not necessary to measure the traction load and give good agreement with the simulation results. With both the simulation and experiment work, the UKF based estimator has shown its capability of monitoring the wheel-rail friction coefficient

Field Oriented Sliding Mode Control of Surface-Mounted Permanent Magnet AC Motors: Theory and Applications to Electrified Vehicles

Permanent magnet ac motors have been extensively utilized for adjustable-speed traction motor drives, due to their inherent advantages including higher power density, superior efficiency and reliability, more precise and rapid torque control, larger power factor, longer bearing, and insulation life-time. Without any proportional-and-integral (PI) controllers, this paper introduces novel first- and higher-order field-oriented sliding mode control schemes. Compared with the traditional PI-based vector control techniques, it is shown that the proposed field oriented sliding mode control methods improve the dynamic torque and speed response, and enhance the robustness to parameter variations, modeling uncertainties, and external load perturbations. While both first- and higher-order controllers display excellent performance, computer simulations show that the higher-order field-oriented sliding mode scheme offers better performance by reducing the chattering phenomenon, which is presented in the first-order scheme. The higher-order field-oriented sliding mode controller, based on the hierarchical use of supertwisting algorithm, is then implemented with a Texas Instruments TMS320F28335 DSP hardware platform to prototype the surface-mounted permanent magnet ac motor drive. Last, computer simulation studies demonstrate that the proposed field-oriented sliding mode control approach is able to effectively meet the speed and torque requirements of a heavy-duty electrified vehicle during the EPA urban driving schedule

Control strategies for blended braking in road vehicles. A study of control strategies for blended friction and regenerative braking in road vehicles based on maximising energy recovery while always meeting the driver demand.

In HEV and EV, higher fuel economy is achieved by operating the ICE and electric motor in the most efficient region and by using regenerative braking. Such a braking system converts, transfers, stores and reuses kinetic energy which would otherwise be dissipated as heat through friction brakes to the environment. This research investigates the control of braking for a mixed-mode braking system in a these vehicles based on the proportion of braking energy that can be stored. Achieving mixed-mode braking requires the ‘blending’ of the two systems (regenerative and friction), and in brake blending, the electric motor/generator (M/G) and the hydraulic actuation pressure are controlled together to meet the driver’s braking demand. The research presented here has established a new robust dynamic modelling procedure for the design of combined regenerative and hydraulic braking systems. Direct torque control and pressure control were selected as the control criteria in both brakes. Two simulation models have been developed in Matlab/Simulink to generate analysis the performance of the control strategy in the blended braking system. Integration of the regenerative braking system with ABS has also been completed, based on two conditions, with and without the deactivation of the regenerative braking. Verification of the models is presented, based on experimental work on two EVs manufactured by TATA Motors; the ACE light commercial vehicle and the VISTA small passenger car. It is concluded that braking demand and vehicle speed determine the operating point of the motor/generator and hence the regenerative braking ratio

Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios