123 research outputs found

    Model Predictive Climate Control for Connected and Automated Vehicles

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    Emerging connected and automated vehicle (CAV) technologies are improving vehicle safety and energy efficiency to the next level and creating unprecedented opportunities and challenges for the control and optimization of the vehicle systems. While previous studies have been focusing on improving the fuel efficiency via powertrain optimizations, vehicle thermal management and its interaction with powertrain control in hot and cold weather conditions have not been fully explored. For light-duty vehicles, the power used by the climate control system usually represents the most significant thermal load. It has been shown that the thermal load imposed by the climate control system may lead to dramatic vehicle range reduction, especially for the vehicles with electrified powertrains. Besides its noticeable impact on vehicle range reduction, the performance of the climate control system also has a direct influence on occupant comfort and customer satisfaction. Aiming at reducing the energy consumption and improving the occupant thermal comfort (OTC) level for the automotive climate control system, this dissertation takes air conditioning (A/C) system as an example and is dedicated to developing practical A/C management strategies for electrified vehicles. In particular, the proposed strategies leverage the predictive information enabled by the CAV technologies such as the traffic and weather predictions. There are three novel MPC-based A/C management strategies developed in this dissertation, the hierarchical optimization, the precision cooling strategy (PCS), and the combined energy and comfort optimization (CECO). They can be differentiated by their OTC assumptions, robustness considerations, and implementation complexities on the testing vehicle. In the hierarchical optimization, a two-layer hierarchical MPC (H-MPC) scheme is exploited for potential integration between the A/C and the powertrain systems of an HEV. This hierarchical structure handles the timescale difference between power and thermal systems as well as the uncertainties associated with long prediction horizon. Comprehensive simulation results over different driving cycles have demonstrated the energy saving potentials of efficient A/C energy management, which is attributes to leveraging the vehicle speed sensitivity of the A/C system efficiency. In terms of the comfort metric, the average cabin air temperature is applied. In contrast to this hierarchical optimization, PCS and CECO utilize the simpler single-layer MPC structure assuming accurate predictive information. They are focusing on formulating more practical OTC metrics and the implementation on the testing vehicle. Specifically, the PCS renders the simplest control-oriented model structure and its energy benefits are validated based on an industrial-level A/C system model. The proposed PCS exploits a more practical comfort metric, DACP, which directly motivates the design of an off-line eco-cooling strategy, which coordinates the A/C operation with respect to the vehicle speed. Vehicle-level energy saving is confirmed according to repeatable vehicle experiments. Finally, the CECO strategy considers a comprehensive OTC model, PMV, and combines the energy and comfort optimizations simultaneously. Further energy saving and OTC improvement can be achieved by explicitly leveraging both traffic and weather predictive information.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153481/1/autowang_1.pd

    Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles

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    This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time

    Parabolic Dish Solar Thermal Power Annual Program Review Proceedings

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    The results of activities of the parabolic dish technology and applications development element of DOE's Solar Thermal Energy System Program are presented. Topics include the development and testing of concentrators, receivers, and power conversion units; system design and development for engineering experiments; economic analysis and marketing assessment; and advanced development activities. A panel discussion concerning industrial support sector requirements is also documented

    Proceedings: Fourth Parabolic Dish Solar Thermal Power Program Review

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    The results of activities within the parabolic dish technology and applications development program are presented. Stirling, organic Rankine and Brayton module technologies, associated hardware and test results to date; concentrator development and progress; economic analyses; and international dish development activities are covered. Two panel discussions, concerning industry issues affecting solar thermal dish development and dish technology from a utility/user perspective, are also included

    State machine-based architecture to control system processes in a hybrid fuel cell electric vehicle

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    This paper presents the development and implementation of a system supervisory controller in a hydrogen-based fuel cell electric vehicle. The controller's primary function is to ensure the safe control of the fuel cell system processes while facilitating coordination among various subsystems, including the balance of plant subsystems, vehicle control unit, diagnosis unit, and powertrain. The supervisory controller comprises of three primary parts: a State Machine, an Optimal Setpoint Generator, and a Power Limit Calculator. The State Machine, which serves as the central part of the supervisory controller, coordinates the fuel cell system's different operational states, including the complex processes of start-up and shutdown. To maximize the fuel cell system's efficiency and minimize the stack's degradation, the Optimal Setpoint Generator produces the subsystem's setpoints by solving an optimization problem and considering the manufacturer's requirements. The Power Limit Calculator assesses the stack's power output capability and calculates the current setpoint for the DC/DC converter. It then provides this data to the Energy Management System (EMS), which oversees the distribution of power between the fuel cell system and the batteries. The proposed fuel cell system supervisory controller is verified using the Worldwide Harmonized Light Vehicles Test Cycles (WLTC) in a real-world car. The designed control structure is implemented in a prototype hydrogen-based electric car at both PowerCell and CEVT facilities under the framework of the INN-BALANCE Horizon 2020 European project

    Control of Energy Storage

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    Energy storage can provide numerous beneficial services and cost savings within the electricity grid, especially when facing future challenges like renewable and electric vehicle (EV) integration. Public bodies, private companies and individuals are deploying storage facilities for several purposes, including arbitrage, grid support, renewable generation, and demand-side management. Storage deployment can therefore yield benefits like reduced frequency fluctuation, better asset utilisation and more predictable power profiles. Such uses of energy storage can reduce the cost of energy, reduce the strain on the grid, reduce the environmental impact of energy use, and prepare the network for future challenges. This Special Issue of Energies explore the latest developments in the control of energy storage in support of the wider energy network, and focus on the control of storage rather than the storage technology itself

    NASA Tech Briefs, October 2001

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    Topics include: special coverage section on composites and plastics, electronic components and systems, software, mechanics, physical sciences, information sciences, book and reports, and a special sections of Photonics Tech Briefs and Motion Control Tech Briefs

    NASA Tech Briefs, July 1991

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    Topics include: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences
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