Control algorithms for e-car

Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.The aim of this work is to design and implement energy consumption optimization control algorithms for electric vehicle. The main objective is to optimize the power-split-ratio between the main power source (batteries) and the super-capacitors during the driving cycle. The driving power profile is estimated and predicted using 3D geographic data and vehicle model. In the first part, vehicle components modelling is introduced. Then, moving average based algorithm and dynamic programming algorithm are presented. Simulations and analysis are provided to show algorithms' benefits. In the last part, Java implementation and also Android operating system application are described.

A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure

Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure. The model is based on the classical minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin

A Northern Tablelands Whole-Farm Linear Program for Economic Evaluation of New Technologies at the Farm-Level

The benefits of evaluating a new technology in a whole-farm context using a linear programming framework are well known. Linear programming allows the joint evaluation of concurrent farm activities, while considering the costs and returns of all enterprises and any resource adjustments imposed by adoption of the technology. This Report provides a rationale for and description of a whole-farm linear programming model that can be used for the economic evaluation of new technologies that are applicable to beef/sheep grazing farms typical of the Northern Tablelands of New South Wales. In this farming system, the whole-farm focus incorporates various aspects of the pasture base, resource constraints and sheep and cattle interactions. An overview of economic tools that are available to assess technologies at the farm level is provided first, listing some of the major benefits and limitations of each of these various techniques. A representative farm for the selected farming system is then developed and a whole-farm linear program based on this representative farm is described in some detail. A series of modelling experiments is undertaken to examine variations of the base model and their impact on the resulting technology evaluation. An example technology, involving the genetic improvement of beef cattle for improved feed efficiency (NFE), is evaluated. The optimal farm plan for a "typical" (single) year is generated, given the objective of maximising farm total gross margin. Three enterprises are selected: 1,108 first-cross ewes, 1,732 Merino wethers and a beef herd of 127 cows producing 18 month old heavy feeder steers (HFS) at 448kg liveweight and excess heifers sold as 9 month old weaners. For this farm plan, the annual operating budget shows a total gross margin for the farm of $86,191. The optimal farm plan for the representative farm is found to be sensitive to relatively small changes in input or output prices and production parameters. Only small improvements in a number of the individual enterprise gross margins would result in them displacing the currently selected enterprises. These results suggest relatively similar profitability levels between these sheep and beef enterprises. This would be anticipated given that all the enterprises described in this report were identified by local experts as being common in the Northern Tablelands. Further, the relatively small differences in enterprise profitability when viewed in a whole farm context also reflect the similar resources that each of the enterprises require, making them readily substitutable. For new technologies that have dynamic attributes, measuring the cashflow over time becomes important. Genetic traits in ruminants that have long biological lags are such technologies. This means that a single-year equilibrium model will be unable to effectively measure the costs of introducing the new technology over time. In the case of the NFE technology in beef cattle, any herd expansion that is possible as a result of the trait is measured by the opportunity cost of heifer sales forgone that are instead retained to increase the breeding herd. These herd dynamics can be represented explicitly within a multi-period version of a whole-farm LP model. The NFE cow enterprise is offered to the model, with the initial sheep enterprises set the same as the base case (1,108 prime lamb producing ewes, 1,732 19-micron Merino wethers). The model again selects 127 HFS producing cows in the first year, but the new optimal farm plan is to invest in the new technology by purchasing NFE-superior bulls in successive years and expanding the cow herd while concurrently decreasing the scale of the Merino wether enterprise. Substitution of Merino wethers for NFE cows occurs up to year 12 after which additional breeding cows are possible from their increasing net feed efficiency alone. There is an increase in cow numbers of 12.6 per cent by year 25, which equates to an improvement in the NPV per breeding cow per year over the base herd of$5.02, using a 5 per cent discount rate. Other experiments reported include adding constraints for fixed costs, family drawings and an overdraft facility; alternate discount rates for the NPV calculations; alternate terminal values for the livestock assets at the end of the simulation period; and a post-optimality risk analysis. This study has highlighted several additional benefits of evaluating a technology in a whole-farm multi-period linear programming framework. First, apart from determining the type and size of the optimal farm enterprise mix and the optimal value of the objective function, whole-farm multi-period linear programming also provides important additional information including shadow costs and prices and constraint slacks, and how they change over time. Shadow costs of activities show how sensitive the optimal farm enterprise mix is to changes in the gross margins of alternate farm activities not included in the current farm plan. The shadow prices for resources indicates how much a farm manager could pay for additional units of a limiting resource, for example, additional labour. Second, in terms of the specific NFE technology examined in this report, it would appear that there may well be regions where such feed efficiencies may be of greater benefit due to particularly large variations in pasture growth patterns throughout the year. The Northern Tablelands with its recognised winter feed deficit may be one such area. This information may be of benefit to researchers in extending the NFE technology to farmers. Third, the deterministic multi-period version of the model highlighted the impact of the inclusion of overhead and capital constraints in the modelling process in determining the potential adoption of a technology by a farm manager. The availability and cost of capital is shown to influence the extent to which the NFE technology may be adopted by an individual farm business. Fourth, from a modelling perspective, the effect of uncertain terminal values and the bearing that they have on measuring the level of adoption of a new technology is an area for further investigation. Finally, the impact of risk was assessed in this study post-optimally by the inclusion of stochastic output prices in the optimal whole farm budgets. This is an area for further research, including the potential of alternate modelling techniques such as MOTAD programming or stochastic dynamic programming. However due to size constraints, such approaches may necessitate trade-offs in terms of the detail of whole-farm models to which they are applied.Research and Development/Tech Change/Emerging Technologies,

Farm-level Economic Evaluation of Net Feed Efficiency in Australia’s Southern Beef Cattle Production System: A Multi-period Linear Programming Approach

Selection of beef cattle for increased net feed efficiency is a current major focus for research. At present the trait seems to be more apparent in Australia’s southern beef production system which is dominated by mixed farming enterprises. Farm-level evaluation of net feed efficiency should take account of the farming system for which it is proposed along with the dynamic nature of genetic selection. Gross margin, linear programming and multi-period linear programming approaches to evaluation of the trait at the farm-level using a representative farm are compared. Implications of the trait for researchers and beef producers are identifiedfarm-level evaluation, genetic traits, linear programming, Farm Management,

The Dynamics of Efficiency and Productivity Growth in U. S. Electric Utilities

This study recognizes explicitly the efficiency gain or loss as a source in explaining the growth. A theoretically consistent method to estimate the decomposition of dynamic total factor productivity growth (TFP) in the presence of inefficiency is developed which is constructed from an extension of the dynamic TFP growth, adjusted for deviations from the long-run equilibrium within an adjustment cost framework. The empirical case study is to U.S. electric utilities, which provides a measure to evaluate how different electric utilities participate in the deregulation of electricity generation. TFP grew by 2.26 percent per annum with growth attributed to the combined scale effects of 0.34 percent, the combined efficiency effects of 0.69 percent, and the technical change effect of 1.22 percent. The dynamic TFP grew by 1.66 percent per annum for electric utilities located within states with the deregulation plan and 3.30 percent per annum for those located outside. Electric utilities located within states with the deregulation plan increased the outputs by improving technical and input allocative efficiencies more than those located outside of states with deregulation plans.productivity growth, adjustment costs, dynamic duality, inefficiency, decomposition, deregulation, e

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Modeling water resources management at the basin level: review and future directions

Water quality / Water resources development / Agricultural production / River basin development / Mathematical models / Simulation models / Water allocation / Policy / Economic aspects / Hydrology / Reservoir operation / Groundwater management / Drainage / Conjunctive use / Surface water / GIS / Decision support systems / Optimization methods / Water supply

Energy demand models for policy formulation : a comparative study of energy demand models

This paper critically reviews existing energy demand forecasting methodologies highlighting the methodological diversities and developments over the past four decades in order to investigate whether the existing energy demand models are appropriate for capturing the specific features of developing countries. The study finds that two types of approaches, econometric and end-use accounting, are used in the existing energy demand models. Although energy demand models have greatly evolved since the early 1970s, key issues such as the poor-rich and urban-rural divides, traditional energy resources, and differentiation between commercial and non-commercial energy commodities are often poorly reflected in these models. While the end-use energy accounting models with detailed sector representations produce more realistic projections compared with the econometric models, they still suffer from huge data deficiencies especially in developing countries. Development and maintenance of more detailed energy databases, further development of models to better reflect developing country context, and institutionalizing the modeling capacity in developing countries are the key requirements for energy demand modeling to deliver richer and more reliable input to policy formulation in developing countries.Energy Production and Transportation,Energy Demand,Environment and Energy Efficiency,Energy and Environment,Economic Theory&Research