PREDICTIVE ENERGY MANAGEMENT IN SMART VEHICLES: EXPLOITING TRAFFIC AND TRAFFIC SIGNAL PREVIEW FOR FUEL SAVING

This master thesis proposes methods for improving fuel economy and emissions of vehicles via use of future information of state of traffic lights, traffic flow, and deterministic traffic flow models. The first part of this thesis proposes use of upcoming traffic signal information within the vehicle\u27s adaptive cruise control system to reduce idle time at stop lights and lower fuel use. To achieve this goal an optimization-based control algorithm is formulated for each equipped vehicle that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle. The objectives are timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed. Three example simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time. The second part of this thesis addresses the use of traffic flow information to derive the fuel- or time-optimal velocity trajectory. A vehicle\u27s untimely arrival at a local traffic wave with lots of stops and goes increases its fuel use. This paper proposes predictive planning of the vehicle velocity for reducing the velocity transients in upcoming traffic waves. In this part of the thesis macroscopic evolution of traffic pattern along the vehicle route is first estimated by combining a traffic flow model and real-time traffic data streams. The fuel optimal velocity trajectory is calculated by solving an optimal control problem with the spatiotemporally varying constraint imposed by the traffic. Simulation results indicatethe potential for considerable improvements in fuel economy with a little compromise on travel time

Network Reconstruction of Dynamic Biological Systems

Inference of network topology from experimental data is a central endeavor in biology, since knowledge of the underlying signaling mechanisms a requirement for understanding biological phenomena. As one of the most important tools in bioinformatics area, development of methods to reconstruct biological networks has attracted remarkable attention in the current decade. Integration of different data types can lead to remarkable improvements in our ability to identify the connectivity of different segments of networks and to predict events within a cellular system. Several recent studies used data integration to reconstruct biochemical networks and to build predictive models from large-scale datasets. In this dissertation we first prescribe directions to reconstruct biological networks based on data properties and priorities in terms of network reconstruction performance. We use experimentally measured and synthetic data sets to compare three popular methods--principal component regression (PCR), linear matrix inequalities (LMI), and Least Absolute Shrinkage and Selection Operator (LASSO)-- in terms of root-mean-squared-error (RMSE), average fractional error in the value of the coefficients, accuracy, sensitivity, specificity and the geometric mean of sensitivity and specificity. This comparison enables us to establish criteria for selection of an appropriate approach for network reconstruction based on a priori properties of experimental data. Reconstruction of biological and biochemical networks from large biological datasets is challenging when the data in question are dynamic. To contribute to this challenge, we also developed a new method, called Doubly Penalized Linear Absolute Shrinkage and Selection Operator (DPLASSO), for reconstruction of dynamic biological networks. DPLASSO consists of two components, statistical significance testing of model coefficients and penalized/constrained optimization. A partial least squares with statistical significance testing acts as a supervisory-level filter to extract the most informative components of the network from a dataset (Layer 1). Then, LASSO with extra weights on the smaller parameters identified in the first layer is employed to retain the main predictors and to set the smallest coefficients to zero (Layer 2). We illustrate that DPLASSO outperforms LASSO in terms of sensitivity, specificity and accuracy. Most of biological systems are nonlinear, therefore, expressing the network model in linear form may not be able to appropriately represent the real structure of the network or to predict the response of the network as accurately as a proper nonlinear model does. Accordingly, as another contribution we have introduced a novel method to reconstruct nonlinear biological networks. In this method, we use a quadratic nonlinear model as the representation of second-order Taylor series expansion of a nonlinear system around an arbitrary point of interest. We apply LASSO to shrink some of the small coefficients to zero. A statistical significance testing (t-test) will complete the parameter (network link) selection. We demonstrate that our proposed approach will lead to considerable improvements in predicting the response of the system and fair improvement in accuracy and sensitivity of the network identifie

Smart Readiness Indicator Part I: An Overview

Buildings are the main sector in accomplishment of the EU energy and climate targets as well as the long-term sustainability goals by 2050 as they consume 40% of EU’s final energy (read: the largest single energy consumer in Europe). Therefore, EU policies and strategies aim to not only improve the efficient use of energy in existing buildings and encourage the use of renewable energy sources, throughout their renovation process, but also reinforce the energy performance of new buildings, integrating smart technologies. The revised Energy Performance of Buildings Directive requires the development of an optional Common Union scheme for rating the smart readiness of buildings, so called the Smart Readiness Indicator. The Smart readiness indicator is an EU project proposed to raise awareness about the advantages of integrating smart technologies in buildings. This paper briefly presents the smart readiness indicator and its improvement so far

Smart Readiness Indicator Part II: Assessing Different Demand Controlled Ventilation Strategies

Building sector is responsible for a large proportion of global energy consumption. Among all building services, Heating, Ventilation and Air Conditioning (HVAC) systems are significantly in charge for building energy use. In HVAC, ventilation is the key issue for providing suitable Indoor Air Quality (IAQ), while it is also responsible for energy consumption. Therefore, enhancing ventilation systems with integrating smart ready technologies not only improves energy efficiency, but also provides better indoor climate for the inhabitants and lowers the possibility of health issues in buildings. As mentioned in the first part of this article, the revised Energy Performance of Buildings Directive requires the development of an optional Common Union scheme for rating the smart readiness of buildings, so called the Smart Readiness Indicator. The Smart readiness indicator is an EU project proposed to raise awareness about the advantages of integrating smart technologies in buildings. This paper presents an assessment of Smart readiness indicator for five different ventilation strategies which were previously developed and simulated by the authors. The results show the potential of smart readiness indicator methodology to be used as an indicator for assessing building energy performance as well as its occupants’ comfort while there are still some shortcomings that have to be covered during the second support study

Measuring vection: A review and critical evaluation of different methods for quantifying illusory self-motion

The sensation of self-motion in the absence of physical motion, known as vection, has been scientifically investigated for several decades. As reliable, objective measures of vection have yet to emerge, researchers have typically employed a variety of subjective methods to quantify the phenomenon of vection. These measures can be broadly categorized into quantitative (e.g., intensity rating scales, magnitude estimation), chronometrical (e.g., onset time/latency, duration), or indirect (e.g., distance travelled) measures. The present review provides an overview and critical evaluation of the most utilized vection measures to date and assesses their respective merit. Furthermore, recommendations for the selection of the most appropriate vection measures will be provided to assist with the process of vection research and to help improving the comparability of research findings across different vection studies

A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles

This paper systematically reviews the techniques and dynamics to study uncertainty modelling in the electric grids considering electric vehicles with vehicle-to-grid integration. Uncertainty types and the most frequent uncertainty modelling approaches for electric vehicles are outlined. The modelling approaches discussed in this paper are Monte Carlo, probabilistic scenarios, stochastic, point estimate method and robust optimisation. Then, Scopus is used to search for articles, and according to these categories, data from articles are extracted. The findings suggest that the probabilistic techniques are the most widely applied, with Monte Carlo and scenario analysis leading. In particular, 19% of the cases benefit from Monte Carlo, 15% from scenario analysis, and 10% each from robust optimisation and the stochastic approach, respectively. Early articles consider robust optimisation relatively more frequent, possibly due to the lack of historical data, while more recent articles adopt the Monte Carlo simulation approach. The uncertainty handling techniques depend on the uncertainty type and human resource availability in aggregate but are unrelated to the generation type. Finally, future directions are given

A Review of Methodologies for Managing Energy Flexibility Resources in Buildings

The integration of renewable energy and flexible energy sources in buildings brings numerous benefits. However, the integration of new technologies has increased the complexity and despite the progress of optimization algorithms and technologies, new research challenges emerge. With the increasing availability of data and advanced modeling tools, stakeholders in the building sector are actively seeking a more comprehensive understanding of the implementation and potential benefits of energy optimization and an extensive up-to-date survey of optimization in the context of buildings and communities is missing in the literature. This study comprehensively reviews over 180 relevant publications on the management and optimization of energy flexibility resources in buildings. The primary objective was to examine and analyze prior research, with emphasis on the used methods, objectives, and scope. The method of content analysis was used to ensure a thorough examination of the existing literature on the subject. It was concluded that multi-objective optimization is crucial to enhance the utilization of flexible resources within individual buildings and communities. Moreover, the study successfully pinpointed key challenges and opportunities for future research, such as the need for accurate data, the complexity of the optimization process, and the potential trade-offs between different objectives