The ‘hydrogen economy’ represents an energy system in which hydrogen and electricity are the dominant energy carriers for use in transportation applications. The ‘hydrogen economy’ minimizes the use of fossil fuels in order to lower the environmental impact of energy use associated with urban air pollution and climate change. An integrated energy system is required to deal with diverse and distributed energy generation technologies such a wind and solar which require energy storage to level energy availability and demand. A distributed ‘energy hub’ is considered a viable concept in envisioning the structure of an integrated energy system. An energy hub is a system which consists of energy input/output, conversion and storage technologies for multiple energy carriers, and would provide an interface between energy producers, consumers, and the transportation infrastructure. Considered in a decentralized network, these hubs would form the nodes of an integrated energy system or network. In this work, a model of a clean energy hub comprising of wind turbines, electrolyzers, hydrogen storage, a commercial building, and a fleet of plug-in fuel cell vehicles (PFCVs) was developed in MATLAB, with electricity and hydrogen used as the energy carriers. This model represents a hypothetical commercial facility which is powered by a renewable energy source and utilizes a zero-emissions fleet of light duty vehicles. The models developed herein capture the energy and cost interactions between the various energy components, and also calculate the CO2 emissions avoided through the implementation of hydrogen economy principles. Wherever possible, similar models were used to inform the development of the clean energy hub model. The purpose of the modelling was to investigate the interactions between a single energy hub and novel components such as a plug-in fuel cell vehicle fleet (PFCV). The final model reports four key results: price of hub electricity, price of hub hydrogen, total annual costs and CO2 emissions avoided. Three scenarios were analysed: minimizing price of hub electricity, minimizing total annual costs, and maximizing the CO2 emissions avoided. Since the clean energy hub could feasibly represent both a facility located within an urban area as well as a remote facility, two separate analyses were also conducted: an on-grid analysis (if the energy hub is close to transmission lines), and an off-grid analysis (representing the remote scenarios). The connection of the energy hub to the broader electricity grid was the most significant factor affecting the results collected. Grid electricity was found to be generally cheaper than electricity produced by wind turbines, and scenarios for minimizing costs heavily favoured the use grid electricity. However, wind turbines were found to avoid CO2 emissions over the use of grid electricity, and scenarios for maximizing emissions avoided heavily favoured wind turbine electricity. In one case, removing the grid connection resulted in the price of electricity from the energy hub increasing from $82/MWh to $300/MWh. The mean travel distance of the fleet was another important factor affecting the cost modelling of the energy hub. The hub’s performance was simulated over a range of mean travel distances (20km to 100km), and the results varied greatly within the range. This is because the mean travel distance directly affects the quantities of electricity and hydrogen consumed by the fleet, a large consumer of energy within the hub. Other factors, such as the output of the wind turbines, or the consumption of the commercial building, are largely fixed. A key sensitivity was discovered within this range; the results were ‘better’ (lower costs and higher emissions avoided) when the mean travel distance exceeded the electric travel range of the fleet. This effect was more noticeable in the on-grid analysis. This sensitivity is due to the underutilization of the hydrogen systems within the hub at lower mean travel distances. It was found that the greater the mean travel distance, the greater the utilization of the electrolyzers and storage tanks lowering the associated per km capital cost of these components. At lower mean travel distances the utilization of the electrolyzers ranged from 25% to 30%, whereas at higher mean travel distances it ranged from 97% to 99%. At higher utilization factors the price of hydrogen is reduced, since the cost recovery is spread over a larger quantity of hydrogen.

This research focuses on fleet management in freight transportation systems. Effective management requires effective planning and control decisions. Plans are often generated using estimates of how the system will evolve in the future; during execution, control decisions need to be made to account for differences between actual realizations and estimates. The benefits of minimum cost plans can be negated by performing costly adjustments during the operational phase. A planning approach that permits effective control during execution is proposed in this dissertation. This approach is inspired by recent work in robust optimization, and is applied to (i) dynamic asset management and (ii) vehicle routing problems. In practice, the fleet management planning is usually decomposed in two parts; the problem of repositioning empty, and the problem of allocating units to customer demands. An alternative integrated dynamic model for asset management problems is proposed. A computational study provides evidence that operating costs and fleet sizes may be significantly reduced with the integrated approach. However, results also illustrate that not considering inherent demand uncertainty generates fragile plans with potential costly control decisions. A planning approach for the empty repositioning problem is proposed that incorporates demand and supply uncertainty using interval around nominal forecasted parameters. The intervals define the uncertainty space for which buffers need to be built into the plan in order to make it a robust plan. Computational evidence suggests that this approach is tractable. The traditional approach to address the Vehicle Routing Problem with Stochastic Demands (VRPSD) is through cost expectation minimization. Although this approach is useful for building routes with low expected cost, it does not directly consider the maximum potential cost that a vehicle might incur when traversing the tour. Our approach aims at minimizing the maximum cost. Computational experiments show that our robust optimization approach generates solutions with expected costs that compare favorably to those obtained with the traditional approach, but also that perform better in worst-case scenarios. We also show how the techniques developed for this problem can be used to address the VRPSD with duration constraints.

Transportation is one of the most vital services in modern society. It makes most of the other functions of society possible. Real transportation systems are so large and complex that in order to build the science of transportation systems it will be necessary to work in many areas, such as: Modeling, Optimization and Simulation. We are interested in solutions for the so-called fleet-sizing-and-allocation problem (FSAP). Fleet sizing and allocation problems are one of the most interesting and hard to solve logistic problems. A fleet sizing and allocation problem consists of two interdependent parts. The fleet sizing problem is to determine a number of transportation units that optimally balances service requirements against the cost of purchasing and maintaining the transportation units. The allocation problem is dealing with the repositioning of transportation units to serve future transportation demand. To make the fleet sizing and allocation problem a little bit more tractable we concentrate on logistic systems with a special hub-and-spoke structure. We start with a very simple fleet sizing of one-to-one case. This case will cause us to focus attention on several key issues in fleet sizing. Afterwards, the generalization of the one-to-one system is the one-to-many system. As a simple example can serve the continuous time situation where a single origin delivers items to many destinations. For the case that items are produced in a deterministic production cycle and transportation times are stochastic. We also studied a hub-and-spoke problem with continuous time and stochastic demand. To solve this problem, based on Marginal Analysis, we applied queueing theory methods. The investigation of the fleet-sizing-and-allocation problem for hub-and-spoke systems is started for a single-period, deterministic-demand model. In that the model hub has to decide how to use a given number of TU’s to satisfy a known (deterministic) demand in the spokes. We consider two cases: 1. Renting of additional TU’s from outside the system is not possible, 2. Renting of additional TU’s from outside the system is possible. For each case, based on Marginal Analysis, we developed a simple algorithm, which gives us the cost-minimal allocation. Since the multi-period, deterministic demand problem is NP-hard we suggest to use Genetic Algorithms. Some building elements for these are described. For the most general situation we also suggest to use simulation optimization. To realize the simulation optimization approach we could use the software tool “Calculation Assessment Optimization System” (CAOS). The idea of CAOS is to provide a software system, which separates the optimization process from the optimization problem. To solve an optimization problem the user of CAOS has to build up a model of the system to which the problem is related. Furthermore he has to define the decision parameters and their domain. Finally, we used CAOS for two classes of hub-and-spoke system: 1. A single hub with four spokes, 2. A single hub with fifty spokes. We applied four optimizers – a Genetic Algorithm, Tabu Search, Hybrid Parallel and Hybrid Serial with two distributions (Normal Distribution and Exponential Distribution) for a customer interarrival times and their demand.

Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009. / Committee Co-Chair: George L. Nemhauser; Committee Co-Chair: Martin W. P. Savelsbergh; Committee Member: Bruce K. Sawhill; Committee Member: Joel Sokol; Committee Member: Ozlem Ergun. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

The ‘hydrogen economy’ represents an energy system in which hydrogen and electricity are the dominant energy carriers for use in transportation applications. The ‘hydrogen economy’ minimizes the use of fossil fuels in order to lower the environmental impact of energy use associated with urban air pollution and climate change. An integrated energy system is required to deal with diverse and distributed energy generation technologies such a wind and solar which require energy storage to level energy availability and demand. A distributed ‘energy hub’ is considered a viable concept in envisioning the structure of an integrated energy system. An energy hub is a system which consists of energy input/output, conversion and storage technologies for multiple energy carriers, and would provide an interface between energy producers, consumers, and the transportation infrastructure. Considered in a decentralized network, these hubs would form the nodes of an integrated energy system or network. In this work, a model of a clean energy hub comprising of wind turbines, electrolyzers, hydrogen storage, a commercial building, and a fleet of plug-in fuel cell vehicles (PFCVs) was developed in MATLAB, with electricity and hydrogen used as the energy carriers. This model represents a hypothetical commercial facility which is powered by a renewable energy source and utilizes a zero-emissions fleet of light duty vehicles. The models developed herein capture the energy and cost interactions between the various energy components, and also calculate the CO2 emissions avoided through the implementation of hydrogen economy principles. Wherever possible, similar models were used to inform the development of the clean energy hub model. The purpose of the modelling was to investigate the interactions between a single energy hub and novel components such as a plug-in fuel cell vehicle fleet (PFCV). The final model reports four key results: price of hub electricity, price of hub hydrogen, total annual costs and CO2 emissions avoided. Three scenarios were analysed: minimizing price of hub electricity, minimizing total annual costs, and maximizing the CO2 emissions avoided. Since the clean energy hub could feasibly represent both a facility located within an urban area as well as a remote facility, two separate analyses were also conducted: an on-grid analysis (if the energy hub is close to transmission lines), and an off-grid analysis (representing the remote scenarios). The connection of the energy hub to the broader electricity grid was the most significant factor affecting the results collected. Grid electricity was found to be generally cheaper than electricity produced by wind turbines, and scenarios for minimizing costs heavily favoured the use grid electricity. However, wind turbines were found to avoid CO2 emissions over the use of grid electricity, and scenarios for maximizing emissions avoided heavily favoured wind turbine electricity. In one case, removing the grid connection resulted in the price of electricity from the energy hub increasing from $82/MWh to $300/MWh. The mean travel distance of the fleet was another important factor affecting the cost modelling of the energy hub. The hub’s performance was simulated over a range of mean travel distances (20km to 100km), and the results varied greatly within the range. This is because the mean travel distance directly affects the quantities of electricity and hydrogen consumed by the fleet, a large consumer of energy within the hub. Other factors, such as the output of the wind turbines, or the consumption of the commercial building, are largely fixed. A key sensitivity was discovered within this range; the results were ‘better’ (lower costs and higher emissions avoided) when the mean travel distance exceeded the electric travel range of the fleet. This effect was more noticeable in the on-grid analysis. This sensitivity is due to the underutilization of the hydrogen systems within the hub at lower mean travel distances. It was found that the greater the mean travel distance, the greater the utilization of the electrolyzers and storage tanks lowering the associated per km capital cost of these components. At lower mean travel distances the utilization of the electrolyzers ranged from 25% to 30%, whereas at higher mean travel distances it ranged from 97% to 99%. At higher utilization factors the price of hydrogen is reduced, since the cost recovery is spread over a larger quantity of hydrogen.

Thesis--(Licentiate)--Royal Institute of Technology, Stockholm, 2007. / Original thesis t.p. and absdtract on 1 leaf inserted. Includes bibliographical references (p. 107-109).

A variety of new features have been added in the present-day vehicles like a pre-crash warning, the vehicle to vehicle communication, semi-autonomous driving systems, telematics, drive by wire. They demand very high bandwidth from in-vehicle networks. Various electronic control units present inside the automotive transmit useful information via automotive multiplexing. Automotive multiplexing allows sharing information among various intelligent modules inside an automotive electronic system. Optimum functionality is achieved by transmitting this data in real time. The high bandwidth and high-speed requirement can be achieved either by using multiple buses or by implementing higher bandwidth. But, by doing so the cost of the network and the complexity of the wiring in the vehicle increases. Another option is to implement higher layer protocol which can reduce the amount of data transferred by using data reduction (DR) techniques, thus reducing the bandwidth usage. The implementation cost is minimal as only the changes are required in the software and not in hardware. In our work, we present a new data reduction algorithm termed as “Comprehensive Data Reduction (CDR)” algorithm. The proposed algorithm is used for minimization of the bus utilization of CAN bus for a future vehicle. The reduction in the busload was efficiently made by compressing the parameters; thus, more number of messages and lower priority messages can be efficiently sent on the CAN bus. The proposed work also presents a performance analysis of proposed algorithm with the boundary of fifteen compression algorithm, and Compression area selection algorithms (Existing Data Reduction Algorithm). The results of the analysis show that proposed CDR algorithm provides better data reduction compared to earlier proposed algorithms. The promising results were obtained in terms of reduction in bus utilization, compression efficiency, and percent peak load of CAN bus. This Reduction in the bus utilization permits to utilize a larger number of network nodes (ECU’s) in the existing system without increasing the overall cost of the system. The proposed algorithm has been developed for automotive environment, but it can also be utilized in any applications where extensive information transmission among various control units is carried out via a multiplexing bus.

abstract: This dissertation research contributes to the advancement of activity-based travel forecasting models along two lines of inquiry. First, the dissertation aims to introduce a continuous-time representation of activity participation in tour-based model systems in practice. Activity-based travel demand forecasting model systems in practice today are largely tour-based model systems that simulate individual daily activity-travel patterns through the prediction of day-level and tour-level activity agendas. These tour level activity-based models adopt a discrete time representation of activities and sequence the activities within tours using rule-based heuristics. An alternate stream of activity-based model systems mostly confined to the research arena are activity scheduling systems that adopt an evolutionary continuous-time approach to model activity participation subject to time-space prism constraints. In this research, a tour characterization framework capable of simulating and sequencing activities in tours along the continuous time dimension is developed and implemented using readily available travel survey data. The proposed framework includes components for modeling the multitude of secondary activities (stops) undertaken as part of the tour, the time allocated to various activities in a tour, and the sequence in which the activities are pursued. Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2014

I have dealt with fleet management analysis in my thesis. The base of this analysis is monitoring development trends in this area. Development trends are especially mobile phones, software equipment, satellite systems, digital tachographs, fulfilment conditions of emission standards EURO 4 and EURO 5 (technologies EGR and SCR), quality and environment management and last but not least problems in fuel consumption management.