Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio

Screening of energy efficient technologies for industrial buildings' retrofit

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

Performance evaluation and optimal design of supermarket refrigeration systems with supermarket model "SuperSim", Part I: Model description and validation

This is the post-print version of the final paper published in International Journal of Refrigeration. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.Conventional supermarket refrigeration systems are responsible for considerable CO2 emissions due to high energy consumption and large quantities of refrigerant leakage. In the effort to conserve energy and reduce environmental impacts, an efficient design tool for the analysis, evaluation and comparison of the performance of alternative system designs and controls is required. This paper provides a description of the modelling procedure employed in the supermarket simulation model ‘SuperSim’ for the simulation of the performance of centralised vapour compression refrigeration systems and their interaction with the building envelope and HVAC systems. The model which has been validated against data from a supermarket has been used for the comparison of R404A and CO2 refrigeration systems and the optimisation of the performance of transcritical CO2 systems. These results are presented in Part II of the paper.DEFR

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Solar cooling: a case study

Throughout the years various methods for heat prevention and indoor temperatures control in the summer have been used. The alternative cooling strategies are based on various passive and low energy cooling technologies for protection of the buildings via design measures or special components to moderate the thermal gains, or to reject the excess heat to the ambient environment. All these techniques aim to reduce summer cooling loads and electricity demand for air conditioning. During the summer the demand for electricity increases because of the extensive use of heating ventilation air conditioning (HVAC) systems, which increase the peak electric load, causing major problems in the electric supply. The energy shortage is worse during ‘dry’ years because of the inability of the hydroelectric power stations to function and cover part of the peak load. The use of solar energy to drive cooling cycles for space conditioning of most buildings is an attractive concept, since the cooling load coincides generally with solar energy availability and therefore cooling requirements of a building are roughly in phase with the solar incidence. Solar cooling systems have the advantage of using absolutely harmless working fluids such as water, or solutions of certain salts. They are energy efficient and environmentally safe. The purpose of this paper is to describe a Solar Cooling System to be installed on the roof of a building in Rome, the headquarters of the State Monopoly. The medium size power plant is composed of the following components: − Solar Collectors; − Thermal Storage Tank; − Absorption Chiller; The plant design is based on a dynamic simulation in TRNSYS, a dynamic simulation tool used by engineers all over the world to make energy calculations in a transient state

System-level key performance indicators for building performance evaluation

Quantifying building energy performance through the development and use of key performance indicators (KPIs) is an essential step in achieving energy saving goals in both new and existing buildings. Current methods used to evaluate improvements, however, are not well represented at the system-level (e.g., lighting, plug-loads, HVAC, service water heating). Instead, they are typically only either measured at the whole building level (e.g., energy use intensity) or at the equipment level (e.g., chiller efficiency coefficient of performance (COP)) with limited insights for benchmarking and diagnosing deviations in performance of aggregated equipment that delivers a specific service to a building (e.g., space heating, lighting). The increasing installation of sensors and meters in buildings makes the evaluation of building performance at the system level more feasible through improved data collection. Leveraging this opportunity, this study introduces a set of system-level KPIs, which cover four major end-use systems in buildings: lighting, MELs (Miscellaneous Electric Loads, aka plug loads), HVAC (heating, ventilation, and air-conditioning), and SWH (service water heating), and their eleven subsystems. The system KPIs are formulated in a new context to represent various types of performance, including energy use, peak demand, load shape, occupant thermal comfort and visual comfort, ventilation, and water use. This paper also presents a database of system KPIs using the EnergyPlus simulation results of 16 USDOE prototype commercial building models across four vintages and five climate zones. These system KPIs, although originally developed for office buildings, can be applied to other building types with some adjustment or extension. Potential applications of system KPIs for system performance benchmarking and diagnostics, code compliance, and measurement and verification are discussed

Energy aspects and ventilation of food retail buildings

Worldwide the food system is responsible for 33% of greenhouse gas emissions. It is estimated that by 2050, the total food production should be 70% more than current food production levels. In the UK, food chain is responsible for around 18% of final energy use and 20% of GHG emissions. Estimates indicate that energy savings of the order of 50% are achievable in food chains by appropriate technology changes in food production, processing, packaging, transportation, and consumption. Ventilation and infiltration account for a significant percentage of the energy use in food retail (supermarkets) and catering facilities such as restaurants and drink outlets. In addition, environmental conditions to maintain indoor air quality and comfort for the users with minimum energy use for such buildings are of primary importance for the business owners and designers. In particular, supermarkets and restaurants present design and operational challenges because the heating ventilation and air-conditioning system has some unique and diverse conditions that it must handle. This paper presents current information on energy use in food retail and catering facilities and continues by focusing on the role of ventilation strategies in food retail supermarkets. It presents the results of current studies in the UK where operational low carbon supermarkets are predicted to save 66% of CO2 emissions compared to a base case store. It shows that low energy ventilation strategies ranging from improved envelope air-tightness, natural ventilation components, reduction of specific fan power, ventilative cooling, novel refrigeration systems using CO2 combined with ventilation heat recovery and storage with phase change materials can lead to significant savings with attractive investment return

Waste heat recovery via organic rankine cycle: results of a era-SME technology transfer project

The main goal of the EraSME project “Waste heat recovery via an Organic Rankine Cycle”, completed by partners Howest (Belgium), Ghent University (Belgium) and University of Applied Sciences Stuttgart (Germany) between 1 January 2010 and 31 December 2012, was to find an entrance in Flanders for the Organic Rankine Cycle (ORC) technology in applications with sufficient amounts of waste heat at high enough temperatures. The project was preceded by a similar study that focused on renewable energy sources. Several tools were developed to aid in the viability assessment, the selection, and the sizing of ORC installations. With these methods, a fast determination of feasibility is possible. The outcome is based on the size, nature and temperature of the waste heat stream as well as the electricity price. An estimate can be given of the net power output, the investment costs and the economic feasibility. The tool is linked to a database of ORC manufacturer specifications. Another objective of the project was to keep track of the evolution in ORC market supply, both commercial and precommercial. We also looked beyond the product line of the main manufacturers. Some ORCs are developed for specific applications. ORC technology was benchmarked against alternatives for waste heat recovery, such as: steam turbines, heat pumps and absorption cooling. ORC in or as a combined heat and power (CHP) system was also examined. A laboratory test unit of 10kWe nominal power was installed during the project, which is now used in further research on dynamic behavior and control. It is still the only ORC demonstration unit in Flanders and has been very instructive in introducing representatives from industry, researchers and students to the technology. A considerable part of the project execution consisted of case studies in response to industrial requests from several sectors. Detailed and concrete feasibility studies allowed us to define the current application area of waste heat recovery ORC in a better way. A knowledge center for waste heat recovery (www.wasteheat.eu) was initiated to consolidate the know-how and to advise potential users

Model-based controller design and simulation of a marine chiller

For the past decade, the US Navy has committed to fundamental research and technology development on its next generation of surface ships. The vision is that these warships will be dynamically reconfigurable, energy-efficient, and have state-of-the-art pulsed energy weapons and sensors onboard. These developments represent a significant increase in highly dynamic on-board electrical systems that will produce correspondingly large amounts of dynamic heat generation, which, if not managed properly, will likely produce significant thermal side effects. In previous work, a highly customizable simulation framework has been developed to address thermal management issues across both the mechanical and electrical domains. This software environment is called the Dynamic Thermal Modeling and Simulation (DTMS) framework. The purpose of the current work is to introduce modern control theory into DTMS, thus providing the framework with the ability to control large-scale system simulations. The research reported in this thesis uses control of a marine chiller as a simulation vehicle. Several control strategies were implemented. These included the well-established PID controller as well as a new controller based on optimal control theory. Results for chiller simulations in the case of no-control, PID control, and optimal control are presented here. The comparative effectiveness of these controls in bringing the chiller to startup equilibrium is investigated. Response of the chiller model and the optimal controller to highly dynamic, varying heat loads was tested. The PID controller in DTMS is modeled as a special case of the transfer function control scheme. A PID controller is simple to implement but responses are inherently local and multiple controls in a system or subsystem simulation can easily lead to conflicts. The optimal control problem has been modeled as an Infinite Horizon Linear Quadratic Regulator (LQR) problem. This formulation is not local and does not create undesirable effects in parts of the system that not controlled directly by controller inputs. Using the York 200-ton marine chiller as an example, specific steps required to formulate the LQR problem are documented in this report. Implementation of the LQR controller was demonstrated for the startup to steady-state function of the chiller at full load. Treatment of the optimal controller ends with simulation of the chiller and its LQR controller under the influence of varying dynamic heat loads in a chilled water loop. The heat load variation examined has highly transient characteristics that affect the temperature of the fresh water entering the chiller, as well as the refrigerant pressure and temperature in the evaporator. The LQR formulation is shown to actively adjust to these varying operating points in a smooth and responsive manner.Mechanical Engineerin