Congestion avoidance for recharging electric vehicles using smoothed particle hydrodynamics

In this paper, a novel approach for recharging electric vehicles (EVs) is proposed based on managing multiple discrete units of electric power flow, named energy demand particles (EDPs). Key similarities between EDPs and fluid particles (FPs) are established that allow the use of a smoothed particle hydrodynamics (SPH) method for scheduling the recharging times of EVs. It is shown, via simulation, that the scheduling procedure not only minimizes the variance of voltage drops in the secondary circuits, but it also can be used to implement a dynamic demand response and frequency control mechanism. The performance of the proposed scheduling procedure is also compared with alternative approaches recently published in the literature

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

Intelligent Simulation Modeling of a Flexible Manufacturing System with Automated Guided Vehicles

Although simulation is a very flexible and cost effective problem solving technique, it has been traditionally limited to building models which are merely descriptive of the system under study. Relatively new approaches combine improvement heuristics and artificial intelligence with simulation to provide prescriptive power in simulation modeling. This study demonstrates the synergy obtained by bringing together the "learning automata theory" and simulation analysis. Intelligent objects are embedded in the simulation model of a Flexible Manufacturing System (FMS), in which Automated Guided Vehicles (AGVs) serve as the material handling system between four unique workcenters. The objective of the study is to find satisfactory AGV routing patterns along available paths to minimize the mean time spent by different kinds of parts in the system. System parameters such as different part routing and processing time requirements, arrivals distribution, number of palettes, available paths between workcenters, number and speed of AGVs can be defined by the user. The network of learning automata acts as the decision maker driving the simulation, and the FMS model acts as the training environment for the automata network; providing realistic, yet cost-effective and risk-free feedback. Object oriented design and implementation of the simulation model with a process oriented world view, graphical animation and visually interactive simulation (using GUI objects such as windows, menus, dialog boxes; mouse sensitive dynamic automaton trace charts and dynamic graphical statistical monitoring) are other issues dealt with in the study

Energy and Service Life Management Strategy for a Two-Drive Multi-Speed Electric Vehicle

Regulations of zero emission passenger cars appear on the horizon, and battery electric vehicles (BEV) are the main solution from the current market. It has been a focus of both academia and industry to extend their range. One of the main approaches is to reduce their energy consumption. Recent studies have shown that the two-drive topology and the multi-speed topology help to do so. It is natural to combine both concepts and to design a two-drive multi-speed topology for BEVs. Due to its more than one degree of freedom, an online energy management strategy (EMS) controlling torque set points of both electric motors and target gear positions is necessary to exploit its potential for reducing total energy consumption in real-world applications. There are numerous studies on EMSs for BEVs and hybrid electric vehicles. The overwhelming majority of them shared the same assumption: shift processes are neglectable. Based on the shift duration statistics, the shift processes of the most common transmissions in today’s market are too long to be ignored for an EMS with an operation frequency of at least 1 Hz. How to develop an EMS that considers shift processes? Suppose that an EMS is developed. It controls the powertrain in favour of low energy consumption, and the parts and the components are loaded accordingly. Some parts might fatigue and fail much faster than others, not because of poor construction dimensioning, but because of excessive use. What can an EMS do to prevent such an extreme scenario? Furthermore, is there a general way to design EMSs for multi-drive BEVs? This thesis is initiated by developing an online EMS for a two-drive multi-speed BEV called “Speed4E”, and tends to address the questions raised earlier. A predictive EMS in a Model Predictive Control framework is developed. A hybrid system considering the shift processes is proposed. Based on it and the Hybrid Minimum Principle, a solver and its algorithms are developed. The Principle is chosen for its accuracy and low time complexity, the two most important attributes of an online EMS. Minimizing the instantaneous Hamiltonian in the Principle is mathematically analysed. Several Lemmas that reduce the time complexity considerably are produced. Compared to an EMS that minimizes instantaneous energy consumption and ignores shift processes, the predictive EMS reduces the energy consumption in the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) by 0.26 % and the shift count by 63.41 %. The hybrid system, the predictive EMS and the mathematical analysis are, as far as the author knows, first of their kinds. A novel multi-criteria operation strategy (MCOS) considering powertrain service life is proposed. Thanks to the hybrid system, the influence of the shift processes on fatigue is included. The MCOS extends the powertrain service life by several times but sacrifices the energy consumption. A general multi-drive (at least two) multi-speed electric powertrain is proposed. Its hybrid system is formulated. The Principle is applied to produce the optimality condition. It is showcased, how to modify certain sets and sample space in the formulation to have the general model and problem represent certain electric powertrains. A unified framework to design EMS for the general multi-drive electric powertrain is proposed, where the algorithms developed for the predictive EMS can be applied

Accessibility Design and Operational Considerations in the Development of Urban Aerial Mobility Vehicles and Networks

Urban aerial mobility vehicles and networks have recently gained considerable interest in the aviation community. These small, short-range vehicles with all-electric or hybrid-electric propulsion systems, tailored to metropolitan aerial transportation needs, promise to radically change passenger mobility and cargo distribution in cities. Accessibility issues have not been a major consideration in UAM vehicle and network discussions to date. This paper seeks to help change that

Optimised control of an advanced hybrid powertrain using combined criteria for energy efficiency and driveline vibrations

This thesis discusses a general approach to hybrid powertrain control based on optimisation and optimal control techniques. A typical strategy comprises a high level non-linear control for optimised energy efficiency, and a lower level Linear Quadratic Regulator (LQR) to track the high-level demand signals and minimise the first torsional vibration mode. The approach is demonstrated in simulation using a model of the Toyota Prius hybrid vehicle, and comparisons are made with a simpler control system which uses proportional integral (PI) control at the lower level. The powertrain of the Toyota Prius has a parallel configuration, comprising a motor, engine and generator connected via an epicyclic gear train. High level control is determined by a Power Efficient Controller (PE C) which dynamically varies the operating demands for the motor, engine and generator. The PEC is an integrated nonlinear controller based on an iterative downhill search strategy for optimising energy efficiency and battery state of charge criteria, and fully accounts for the non-linear nature of the various efficiency maps. The PEC demand signals are passed onto the LQR controller where a cost function balances the importance of deviations from these demands against an additional criterion relating to the amplitude of driveline vibrations. System non-linearity is again accounted for at the lower level through gain scheduling of the LQR controller. Controller performance is assessed. in simulation, the results being compared with a reference system that uses simple PI action to deliver low-level control. Consideration is also given to assessing performance against that of a more general, fully non-linear dynamic optimal controller

Energy management with TRIANA on FPAI

The current growth of smart grid capable appliances motivates the development of general and flexible software systems to support these devices. The FlexiblePower Application Infrastructure (FPAI) is such a system, which classifies devices by their type of flexibility. Subsequently, energy applications only have to support these flexibility classes. In this work, we present an implementation of the TRIANA demand side management approach as an energy application on the FPAI energy management software platform. We use dynamic programming to solve the local scheduling problems for each flexibility class. This work shows that FPAI can host energy applications with different control approaches and that the TRIANA control approach can be embedded in a general implementation framework