A Parametric Study Of A Turbogenerator On A Diesel-Electric Hybrid Bus

The fuel consumption of automotive vehicles has become a prime consideration to manufacturers and operators as fuel prices continue to rise steadily, and legislation governing toxic emissions becomes ever more strict. This is particularly true for bus operators as government fuel subsidies are cut or removed.

In an effort to reduce the fuel consumption of a diesel-electric hybrid bus, an exhaust recovery turbogenerator has been selected from a wide ranging literature review as the most appropriate method of recovering some of the wasted heat in the exhaust line. This paper examines the effect on fuel consumption of a turbogenerator applied to a 2.4-litre diesel engine.

A validated one-dimensional engine model created using Ricardo WAVE was used as a baseline, and was modified in subsequent models to include a turbogenerator downstream, and in series with, the turbocharger turbine. A fuel consumption map of the modified engine was produced, and an in-house simulation tool was then used to examine the fuel economy benefit delivered by the turbogenerator on a bus operating on various drive-cycles.

A parametric study is presented which examined the performance of turbogenerators of various size and power output. The operating strategy of the turbogenerator was also discussed with a view to maximising turbine efficiency at each operating point.

The performance of the existing turbocharger on the hybrid bus was also investigated; both the compressor and turbine were optimised and the subsequent benefits to the fuel consumption of the vehicle were shown.

The final configuration is then presented and the overall improvement in fuel economy of the hybrid bus was determined over various drive-cycles.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Development and evaluation of a range anxiety-reducing business model for connected full electric vehicles

This thesis develops and evaluates a business model for connected full electric vehicles (FEV) for the European market. Despite a promoting political environment, various barriers have thus far prevented the FEV from becoming a mass-market vehicle. Besides cost, the most noteworthy of these barriers is represented by range anxiety, a product of FEVs’ limited range, lacking availability of charging infrastructure, and long recharging times. Connected FEVs, which maintain a constant connection to the surrounding infrastructure, appear to be a promising element to overcome drivers’ range anxiety. Yet their successful application requires a well functioning FEV ecosystem which can only be created through the collaboration of various stakeholders such as original equipment manufacturers (OEM), first tier suppliers (FTS), charging infrastructure and service providers (CISP), utilities, communication enablers, and governments. This thesis explores and evaluates how a business model, jointly created by these stakeholders, could look like, i.e. how stakeholders could collaborate in the design of products, services, infrastructure, and advanced mobility management, to meet drivers with a sensible value proposition that is at least equivalent to that of internal combustion engine (ICE) cars. It suggests that this value proposition will be an end-2-end package provided by CISPs or OEMs that comprises mobility packages (incl. pay per mile plans, battery leasing, charging and battery swapping (BS) infrastructure) and FEVs equipped with an on-board unit (OBU) combined with additional services targeted at range anxiety reduction. From a theoretical point of view the thesis answers the question which business model framework is suitable for the development of a holistic, i.e. all stakeholder-comprising business model for connected FEVs and defines such a business model. In doing so the thesis provides the first comprehensive business model related research findings on connected FEVs, as prior works focused on the much less complex scenario featuring only “offline” FEVs.

AGENT BASED, DISTRIBUTED CONTROL STRATEGIES AND OPTIMIZATION OF PLUG-IN ELECTRIC VEHICLES ON SMART/MICROGRID ARCHITECTURES

This thesis will present strategies for the use of plug-in electric vehicles on smart and microgrids. MATLAB is used as the design tool for all models and simulations. First, a scenario will be explored using the dispatchable loads of electric vehicles to stabilize a microgrid with a high penetration of renewable power generation. Grid components for a microgrid with 50% photovoltaic solar production will be sized through an optimization routine to maintain storage system, load, and vehicle states over a 24-hour period. The findings of this portion are that the dispatchable loads can be used to guard against unpredictable losses in renewable generation output. Second, the use of distributed control strategies for the charging of electric vehicles utilizing an agent-based approach on a smart grid will be studied. The vehicles are regarded as additional loads to a primary forecasted load and use information transfer with the grid to make their charging decisions. Three lightweight control strategies and their effects on the power grid will be presented. The findings are that the charging behavior and peak loads on the grid can be reduced through the use of distributed control strategies.

Evaluation of the utilization of electric vehicles for building energy management in hotels

Governments are working in new policies to slow down total energy consumption and greenhouse gases (GHG) emissions, promoting the deployment of electric vehicles (EVs) in all countries. In order to facilitate this deployment and help to reduce the final costs of their batteries, additional utilization of EVs when those are parked has been proposed. EVs can be used to minimize the total electricity cost of buildings (named vehicle to building applications, V2B). In this paper an economic evaluation of EVs in the Building Energy Management System is shown. The optimal storage capacity and its equivalent number of EVs are determined. This value is then used for determining the optimal charging schedule to be applied to the batteries. From this schedule, the total expected profit is derived for the case of a real hotel in Spain.

Vehicle to vehicle energy exchange in smart grid applications

This paper presents a novel vehicle to vehicle energy exchange market (V2VEE) between electric vehicles (EVs) for decreasing the energy cost to be paid by some users whose EVs must be recharged during the day to fulfil their daily scheduled trips and also reducing the impact of charging on the electric grid. EVs with excess of energy in their batteries can transfer this energy among other EVs which need charge during their daily trips. These second type of owners can buy the energy directly to the electric grid or they can buy the energy from other EV at lower price. An aggregator is responsible for collecting all information among vehicles located in the same area at the same time and make possible this energy transfer.

Educational Project for the Teaching of Control of Electric Traction Drives

Electric vehicles constitute a multidisciplinary subject that involves disciplines such as automotive, mechanical, electrical and control engineering. Due to this multidisciplinary technical nature, practical teaching methodologies are of special relevance. Paradoxically, in the past, the training of engineers specializing in this area has lacked the practical component represented by field tests, due to the difficulty of accessing real systems. This paper presents an educational project specifically designed for the teaching and training of engineering students with different backgrounds and experience. The teaching methodology focuses on the topology of electric traction drives and their control. It includes two stages, a simulation computer model and a scaled laboratory workbench that comprises a traction electrical drive coupled to a vehicle emulator. With this equipment, the effectiveness of different traction control strategies can be analyzed from the point of view of energy efficiency, robustness, easiness of implementation and acoustic noise.

Electric vehicles: limited range and high costs hamper commercialization. Report by the Comptroller General of the United States.

Mode of access: Internet.

Alternative fueled vehicle fleet safety experience: summary report.

National Highway Traffic Safety Administration, Washington, D.C.

NHTSA's Heavy Duty Vehicle Brake Research Program - report Number 10: evaluation of trailer antilock braking systems electrical powering. Interim final report.

National Highway Traffic Safety Administration, Washington, D.C.

FMVSS 212, 219, 301, testing of electric vehicles - Electric Van 600, EVA Pacer. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

FMVSS 212, 219, 301 testing of electric vehicles - Electra Van 1000, Electra Van 1000P. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Report of the Committee on the Movement of the Vehicle /

"To be presented before the American Electric Railway Transportation and Traffic Association convention held at San Francisco, Cal., June 23 to 26, 1930."