6,525 research outputs found

    MODELING OF THERMAL DYNAMICS IN CHEVROLET VOLT GEN II HYBRID ELECTRIC VEHICLE FOR INTEGRATED POWERTRAIN AND HVAC OPTIMAL OPERATION THROUGH CONNECTIVITY

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    Integrated thermal energy management across system level components in electric vehicles (EVs) and hybrid electric vehicles (HEVs) is currently an under explored space. The proliferation of connected vehicles and accompanying infrastructure in recent years provides additional motivation to explore opportunities in optimizing thermal energy management in EVs and HEVs with the help of this newly available connected vehicle data. This thesis aims to examine and analyze the potential to mitigate vehicle energy consumption and extend usable driving range through optimal control strategies which exploit physical system dynamics via controls integration of vehicle subsystems. In this study, data-driven and physics-based models for heating, ventilation and air-conditioning (HVAC) are developed and utilized along with the vehicle dynamics and powertrain (VD\&PT) models for a GM Chevrolet Volt hybrid electric vehicle to enable co-optimization of HVAC and VD\&PT systems of HEVs. The information available in connected vehicles like driver schedules, trip duration and ambient conditions is leveraged along with the vehicle system dynamics to predict operating conditions of the vehicle under study. All this information is utilized to optimize the operation of an integrated HVAC and VD\&PT system in a hybrid electric vehicle to achieve the goal of reduced energy consumption. For achieving the goals outlined for this thesis, an integrated HVAC and VD\&PT model is developed and the various components, sub-systems and systems are validated against real world test data. Then, integrated relationships relevant to the thermal dynamics of an HEV are established. These relationships comprise the combined operational characteristics of the internal combustion (IC) engine coolant and the cabin electric heater for cabin heating, coordinated controls of IC engine using engine coolant and catalyst temperatures for cabin thermal conditioning in cold ambient conditions and the combined operational characteristics of the air-conditioning compressor for conditioning both cabin and high-voltage battery in an HEV. Next, these sub-system and system relationships are used to evaluate potential energy savings in cabin heating and cooling when vehicle\u27s operating schedule is known. Finally, an optimization study is conducted to establish an energy efficient control strategy which maximizes the HVAC energy efficiency whilst maintaining occupant comfort levels according to ASHRAE standards, all while improving the usable range of the vehicle relative to its baseline calibration. The mean energy savings in overall vehicle energy consumption using an integrated HVAC - Powertrain control strategy and a coordinated thermal management strategy proposed in this work are 33\% and 1414\% respectively

    A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

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    In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

    Screening of energy efficient technologies for industrial buildings' retrofit

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    This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

    An Economic Model-Based Predictive Control to Manage the Users' Thermal Comfort in a Building

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    The goal of maintaining users' thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users' productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users' thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased.Spanish Ministry of Science and Innovation [DPI2014-56364-C2-1-R]; EU-ERDF funds; Competitiveness and ERDF funds; Fundacion Iberdrola Espana; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

    Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance

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    Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure
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