APPRAISAL OF TAKAGI–SUGENO TYPE NEURO-FUZZY NETWORK SYSTEM WITH A MODIFIED DIFFERENTIAL EVOLUTION METHOD TO PREDICT NONLINEAR WHEEL DYNAMICS CAUSED BY ROAD IRREGULARITIES

Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tire–obstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tire–obstacle contact interface. A new Takagi–Sugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics

Data-driven approaches for modeling train control models: Comparison and case studies

In railway systems, the train dynamics are usually affected by the external environment (e.g., snow and wind) and wear-out of on-board equipment, leading to the performance degradation of automatic train control algorithms. In most existing studies, the train control models were derived from the mechanical analyzation of train motors and wheel-track frictions, which may require many times of field trials and high costs to validate the model parameters. To overcome this issue, we record the explicit train operation data in Beijing Metro within three years and develop three data-driven approaches, involving a linear regression-based model (LAM), a nonlinear regression-based model (NRM), and furthermore a deep neural network based (DNN) model, where the LAM and NRM can act as benchmarks for evaluating DNN. To improve the training efficiency of DNN model, we especially customize the input and output layers of DNN, batch normalization based layers and network parameter initialization techniques according to the unique characteristics of railway train models. From the model training and testing results with field data, we observe that DNN significantly enhances the predicting accuracy for the train control model by using our customized network structure compared with LAM and NRM models. These data-driven approaches are successfully applied to Beijing Metro for designing efficient train control algorithms

Energy Management Systems for Smart Electric Railway Networks: A Methodological Review

Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO2 emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated

Design and Analysis of a Disc Rotor for a Small Race Car's Braking System

The purpose of these report is to record the information regarding on Final Year Project. It includes the data gathering, calculations and design stage that had been gone through by author. The objective of the project is to design the disc rotor for a braking system of Formula SAE according to rules and regulation of Formula SAE car. FSAE car braking system that is designed by author is based on the dual hydraulic circuit, which the standard front to rear split that generally used in rear wheel driven car. Moreover, the braking system uses a disc braking system instead of using drum brakes. Advantages of using disc brakes system are more fade resistant, possible to stop in wet condition, reduce the overall weight of the car and easy to make service during the race. In the Literature Review, the first stage in designing is made it according by the rules and regulation of Formula SAE guidelines. Moreover, the formulas were identified in getting the right formula of braking performance, component sizing, and adhesion utilizations of the car. In the Result and Discussion, it shown the target of brake bias setting is 55:45 (front to rear) has been calculated in the Excel form. Also, adhesion utilization curve is generated and shown the both axles will lock up simultaneously at deceleration rate, z=k at 0.58g. Next, the rotor have been designed by using CATIA. Also, the rotor's thermal analysis have been analyzed using ANSYS Workbench. The manufacturing process of the rotor have also been proposed by the author. As conclusion, a certain amount of understanding of the brake system has been obtained

A variable weight adaptive cruise control strategy based on lane change recognition of leading vehicle

The traditional adaptive cruise system is responsible for delay in recognizing the cut-in/cut-out behaviour of front vehicle, and there is significant longitudinal acceleration of the vehicle fluctuation leading to reduced driver’s comfort level and even dangerous situation. In this paper, the next generation simulation data set and back propagation (BP) neural network are used to train the vehicle lane change recognition model to recognize the lane change behaviour of the preceding vehicle. The higher controller adopts variable weight linear quadratic optimal control to adjust the weight parameters according to the recognition results of front vehicle to reduce the fluctuation of vehicle acceleration. The lower layer adopts fuzzy proportional-integral-derivative (PID) control to follow the expected acceleration and builds the vehicle inverse dynamic model. Through CarSim/Simulink co-simulation, the results show that, under the cut-in or cut-out and working conditions, the behaviour of the leading vehicle can be recognized, following target can be switched in advance, weight parameters can be adjusted and the large fluctuation of longitudinal acceleration can be reduced

Modelling and Model Predictive Control of Power-Split Hybrid Powertrains for Self-Driving Vehicles

Designing an autonomous vehicle system architecture requires extensive vehicle simulation prior to its implementation on a vehicle. Simulation provides a controlled environment to test the robustness of an autonomous architecture in a variety of driving scenarios. In any autonomous vehicle project, high-fidelity modelling of the vehicle platform is important for accurate simulations. For power-split hybrid electric vehicles, modelling the powertrain for autonomous applications is particularly difficult. The mapping from accelerator and brake pedal positions to torque at the wheels can be a function of many states. Due to this complex powertrain behavior, it is challenging to develop vehicle dynamics control algorithms for autonomous power-split hybrid vehicles. The 2015 Lincoln MKZ Hybrid is the selected vehicle platform of Autonomoose, the University of Waterloo’s autonomous vehicle project. Autonomoose required high-fidelity models of the vehicle’s power-split powertrain and braking systems, and a new longitudinal dynamics vehicle controller. In this thesis, a grey-box approach to modelling the Lincoln MKZ’s powertrain and braking systems is proposed. The modelling approach utilizes a combination of shallow neural networks and analytical methods to generate a mapping from accelerator and brake pedal positions to the torque at each wheel. Extensive road testing of the vehicle was performed to identify parameters of the powertrain and braking models. Experimental data was measured using a vehicle measurement system and CAN bus diagnostic signals. Model parameters were identified using optimization algorithms. The powertrain and braking models were combined with a vehicle dynamics model to form a complete high-fidelity model of the vehicle that was validated by open-loop simulation. The high-fidelity models of the powertrain and braking were simplified and combined with a longitudinal vehicle dynamics model to create a control-oriented model of the vehicle. The control-oriented model was used to design an instantaneously linearizing model predictive controller (MPC). The advantages of the MPC over a classical proportional-integral (PI) controller were proven in simulation, and a framework for implementing the MPC on the vehicle was developed. The MPC was implemented on the vehicle for track testing. Early track testing results of the MPC show superior performance to the existing PI that could improve with additional controller parameter tuning

Metrology Infrastructure for Energy and Power Quality in DC Railway Systems

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

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