282 research outputs found

    Evaporator Modeling and an Optimal Control Strategy Development of an Organic Rankine Cycle Waste Heat Recovery System for a Heavy Duty Diesel Engine Application

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    The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system and maximizing the heat recovery is a challenge for real time application. To that end, this research resulted in the following main developments. The first new development is in the area of heat exchanger modeling. The heat exchanger is a key component within the WHR system and it governs the dynamics of the complete ORC system. The heat exchanger model is extended using a thermal image data to improve its phase length prediction capability. It’s shown that the new identified empirical equations help improve the phase length estimation by 43% over a set of transient experiments. As a result, the model can be used to develop an improved control oriented moving boundary model and to provide insights into evaporator design. The second new development is the advancement of the control design of an ORC system. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. The proposed approach in this this dissertation is a look-ahead control strategy where, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model. In the simulation study, a reference tracking controller is designed based on the Model Predictive Control (MPC) methodology. Two variants of Non-linear MPC (NMPC) are evaluated: an NMPC with look-ahead exhaust conditions and a baseline NMPC without the knowledge of future exhaust conditions. Simulation results show no particular improvement to working fluid superheat tracking at the evaporator outlet via the look-ahead strategy for a drive cycle. However, the look-ahead control strategy does provide a substantial reduction in system control effort via dampening the heavily transient working fluid pump actuation, enhancing pump longevity, health, and reducing pump power consumption. This reduction in pump actuation helps the NMPC with preview to maintain the superheat lower than the NMPC without this feature for certain frequency of the exhaust conditions. Overall, NMPC with preview feature can help reduce parasitic losses, like pump power and improve power generation. The third development addresses the modeling errors and measurement inaccuracies for NMPC implementation. NMPC is inherently a state feedback system and for that reason an Extended Kalman Filter (EKF) is used to estimate unmeasurable states inside the ORC evaporators based on exhaust gas and working fluid temperatures. Since it is not realistic to expect that the system model will perfectly describe the behavior of the evaporator dynamics in all operating conditions, the estimator is therefore augmented with a disturbance model for offset free MPC tracking. Simulation study shows that the augmented system is perfectly capable of discarding the model errors and rejecting the measurement inaccuracies. Moreover, experimental validation confirms that no steady state error is observed during online implementation of the augmented EKF. Finally, experimental validation of the designed NMPC control strategy was conducted. The performance of the NMPC was evaluated on a heavily transient drive cycle, as well as on a sinusoidal generated heat signals. Both experimental and simulated sinusoidal exhaust condition shows that evaporator under consideration inherently helps attenuate the fluctuating exhaust conditions due to its thermal inertia especially for heat signals of shorter time periods. However for slow changing exhaust conditions, a slower rate of change of working fluid flow helps in inhibiting temperature overshoot at the evaporator outlet

    Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation

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    In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithm − based on a recent study on how well a fruit fly’s tiny brain finds food − was developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity

    Active suspension control of electric vehicle with in-wheel motors

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    In-wheel motor (IWM) technology has attracted increasing research interests in recent years due to the numerous advantages it offers. However, the direct attachment of IWMs to the wheels can result in an increase in the vehicle unsprung mass and a significant drop in the suspension ride comfort performance and road holding stability. Other issues such as motor bearing wear motor vibration, air-gap eccentricity and residual unbalanced radial force can adversely influence the motor vibration, passenger comfort and vehicle rollover stability. Active suspension and optimized passive suspension are possible methods deployed to improve the ride comfort and safety of electric vehicles equipped with inwheel motor. The trade-off between ride comfort and handling stability is a major challenge in active suspension design. This thesis investigates the development of novel active suspension systems for successful implementation of IWM technology in electric cars. Towards such aim, several active suspension methods based on robust H∞ control methods are developed to achieve enhanced suspension performance by overcoming the conflicting requirement between ride comfort, suspension deflection and road holding. A novel fault-tolerant H∞ controller based on friction compensation is in the presence of system parameter uncertainties, actuator faults, as well as actuator time delay and system friction is proposed. A friction observer-based Takagi-Sugeno (T-S) fuzzy H∞ controller is developed for active suspension with sprung mass variation and system friction. This method is validated experimentally on a quarter car test rig. The experimental results demonstrate the effectiveness of proposed control methods in improving vehicle ride performance and road holding capability under different road profiles. Quarter car suspension model with suspended shaft-less direct-drive motors has the potential to improve the road holding capability and ride performance. Based on the quarter car suspension with dynamic vibration absorber (DVA) model, a multi-objective parameter optimization for active suspension of IWM mounted electric vehicle based on genetic algorithm (GA) is proposed to suppress the sprung mass vibration, motor vibration, motor bearing wear as well as improving ride comfort, suspension deflection and road holding stability. Then a fault-tolerant fuzzy H∞ control design approach for active suspension of IWM driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The T-S fuzzy suspension model is used to cope with the possible sprung mass variation. The output feedback control problem for active suspension system of IWM driven electric vehicles with actuator faults and time delay is further investigated. The suspended motor parameters and vehicle suspension parameters are optimized based on the particle swarm optimization. A robust output feedback H∞ controller is designed to guarantee the system’s asymptotic stability and simultaneously satisfying the performance constraints. The proposed output feedback controller reveals much better performance than previous work when different actuator thrust losses and time delay occurs. The road surface roughness is coupled with in-wheel switched reluctance motor air-gap eccentricity and the unbalanced residual vertical force. Coupling effects between road excitation and in wheel switched reluctance motor (SRM) on electric vehicle ride comfort are also analysed in this thesis. A hybrid control method including output feedback controller and SRM controller are designed to suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort. Then a state feedback H∞ controller combined with SRM controller is designed for in-wheel SRM driven electric vehicle with DVA structure to enhance vehicle and SRM performance. Simulation results demonstrate the effectiveness of DVA structure based active suspension system with proposed control method its ability to significantly improve the road holding capability and ride performance, as well as motor performance

    Active Seat Suspensions for Automotive Applications

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    Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective

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    Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet

    Wind farm control technologies : from classical control to reinforcement learning

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    Wind power plays a vital role in the global effort towards net zero. The recent figure shows that 93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year increase. Control system is the core in wind farm operations and has essential influences on the farm’s power capture efficiency, economic profitability, and operation & maintenance cost. However, wind farms’ inherent system complexities and the aerodynamic interactions among wind turbines bring significant barriers to control systems design. The wind industry has recognized that new technologies are needed to handle wind farm control tasks, especially for large-scale offshore wind farms. This paper provides a comprehensive review of the development and most recent advances of wind farm control technologies. This covers the introduction of fundamental aspects in wind farm control in terms of system modelling, main challenges, and control objectives. Existing wind farm control methods for different purposes, including layout optimization, power generation maximization, fatigue loads minimization, and power reference tracking, are investigated. Moreover, a detailed discussion regarding the differences and connections among model-based, model-free and data-driven wind farm approaches is presented. In addition, highlights are made on the state-of-the-art wind farm control technologies based on reinforcement learning - a booming machine learning technique that has drawn worldwide attention. Future challenges and research avenues in wind farm control are also analysed

    A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

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    Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

    A Trip Planning-Assisted Energy Management System for Connected PHEVs: Evaluation and Enhancement

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    The built-in Energy Management System (EMS) of Plug-in Hybrid Electric Vehicles (PHEVs) plays an important role in the fuel efficiency of these vehicles. Recently, it has been revealed that prior knowledge of the upcoming trip can assist EMS to enhance the distribution of power between the energy sources, i.e. the engine and the motor-generators used in PHEVs, resulting in lower fuel consumptions. This dissertation intends to further investigate on a Trip Planning-assisted EMS (TP-assisted EMS), by studying its feasibility for online implementation, and evaluating its performance and robustness with respect to the trip data uncertainties in various practical scenarios, to ultimately answer this question: Does the TP-assisted EMS function as a reliable system for PHEVs which can outperform conventional methods? This research starts with improving upon an existing Trip Planning module with an emphasis on its online integration with the EMS module. In particular, the power-balance model of PHEVs is introduced, which is computationally inexpensive and yet adequately accurate to be used for the optimizations involved in the Trip Planning module. To speed up the optimizations, the use of Particle Swarm Optimization (PSO) algorithm is suggested. These modifications result in the reduction of computational time, making TP-assisted EMS module suitable for online implementations. Once the TP-assisted EMS module has been integrated with a high-fidelity model of the baseline PHEV, namely, 2013 Toyota Prius PHEV, its performance and sensitivity/robustness have been extensively studied through Monte Carlo simulations, where numerous samples of standard as well as real-world drive cycles have been tested. However, in order to use these data for Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) tests, a Micro-trip Generator block has been developed. This block automatically segments the drive cycles, similar to the way that trip information is obtained in practice, making the simulation samples compatible with the Trip Planning module. Statistical analyses of the simulation results show that the TP-assisted EMS is a superior controller compared to the conventional EMS strategies. Moreover, these simulations present one of the first sensitivity analyses that have been performed in the context of TP-assisted EMS for PHEVs, showing that this system is robust despite the existence of random disturbances and meanwhile has low sensitivity against variations of the design parameters

    A METHODOLOGY FOR ENERGY OPTIMIZATION OF BUILDINGS CONSIDERING SIMULTANEOUSLY BUILDING ENVELOPE HVAC AND RENEWABLE SYSTEM PARAMETERS

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    Energy is the vital source of life and it plays a key role in development of human society. Any living creature relies on a source of energy to exist. Similarly, machines require power to operate. Starting with Industrial Revolution, the modern life clearly depends on energy. We need energy for almost everything we do in our daily life, including transportation, agriculture, telecommunication, powering industry, heating, cooling and lighting our buildings, powering electric equipment etc. Global energy requirement is set to increase due to many factors such as rapid industrialization, urbanization, population growth, and growing demand for higher living standards. There is a variety of energy resources available on our planet and non-renewable fossil fuels have been the main source of energy ever since the Industrial Revolution. Unfortunately, unsustainable consumption of energy resources and reliance on fossil fuels has led to severe problems such as energy resource scarcity, global climate change and environmental pollution. The building sector compromising homes, public buildings and businesses represent a major share of global energy and resource consumption. Therefore, while buildings provide numerous benefits to society, they also have major environmental impacts. To build and operate buildings, we consume about 40 % of global energy, 25 % of global water, and 40 % of other global resources. Moreover, buildings are involved in producing approximately one third of greenhouse gas emissions. Today, the stress put on the environment by building sector has reached dangerous levels therefore urgent measures are required to approach buildings and to minimize their negative impacts. We can design energy-efficient buildings only when we know where and why energy is needed and how it is used. Most of the energy consumed in buildings is used for heating, cooling, ventilating and lighting the indoor spaces, for sanitary water heating purposes and powering plug-in appliances required for daily life activities. Moreover, on-site renewable energy generation supports building energy efficiency by providing sustainable energy sources for the building energy needs. The production and consumption of energy carriers in buildings occur through the network of interconnected building sub-systems. A change in one energy process affects other energy processes. Thus, the overall building energy efficiency depends on the combined impact of the building with its systems interacting dynamically all among themselves, with building occupants and with outdoor conditions. Therefore, designing buildings for energy efficiency requires paying attention to complex interactions between the exterior environment and the internal conditions separated by building envelope complemented by building systems. In addition to building energy and CO2 emission performance, there are also other criteria for designers to consider for a comprehensive building design. For instance, building energy cost is one of the major cost types during building life span. Therefore, improving building efficiency not only addresses the challenges of global climate change but also high operational costs and consequent economic resource dependency. However, investments in energy efficiency measures can be costly, too. As a result, the economic viability of design options should be analysed carefully during decision-making process and cost-effective design choices needs to be identified. Furthermore, while applying measures to improve building performance, comfort conditions of occupants should not be neglected, as well. Advances in science and technologies introduced many approaches and technological products that can be benefitted in building design. However, it could be rather difficult to select what design strategies to follow and which technologies to implement among many for cost-effective energy efficiency while satisfying equally valued and beneficial objectives including comfort and environmental issues. Even using the state-of-the-art energy technologies can only have limited impact on the overall building performance if the building and system integration is not well explored. Conventional design methods, which are linear and sequential, are inadequate to address the inter-depended nature of buildings. There is a strong need today for new methods that can evaluate the overall building performance from different aspects while treating the building, its systems and surrounding as a whole and provide quantitative insight information for the designers. Therefore, in the current study, we purpose a simulation-based optimization methodology where improving building performance is taken integrally as one-problem and the interactions between building structure, HVAC equipment and building-integrated renewable energy production are simultaneously and dynamically solved through mathematical optimization techniques while looking for a balanced combination of several design options and design objectives for real-life design challenges. The objective of the methodology is to explore cost-effective energy saving options among a considered list of energy efficiency measures, which can provide comfort while limiting harmful environmental impacts in the long term therefore financial, environmental and comfort benefits are considered and assessed together. During the optimization-based search, building architectural features, building envelope features, size and type of HVAC equipment that belong to a pre-designed HVAC system and size and type of considered renewable system alternatives are explored simultaneously together for an optimal combination under given constraints. The developed optimization framework consists of three main modules: the optimizer, the simulator, and a user-created energy efficiency measures database. The responsibility of the optimizer is to control the entire process by implementing the optimization algorithm, to trigger simulation for performance calculation, to assign new values to variables, to calculate objective function, to impose constraints, and to check stopping criteria. The optimizer module is based on GenOpt optimization environment. However, a sub-module was designed, developed and added to optimization structure to enable Genopt to communicate with the user-created database module. Therefore, every time the value of a variable is updated, the technical and financial information of a matching product or system equipment is read from the database, written into simulation model, and fed to the objective formula. The simulator evaluates energy-related performance metrics and functional constraints through dynamic simulation techniques provided by EnergyPlus simulation tool. The database defines and organizes design variables and stores user-collected cost related, technical and non-technical data about the building energy efficiency measures to be tested during the optimization. An updated version of Particle Swarm Optimization with constriction coefficient is used as the optimization algorithm. The study covers multi-dimensional building design aims through a single-objective optimization approach where multi objectives are represented in a ε-Constraint penalty approach. The primary objective is taken as minimization of building global costs due to changes in design variables therefore it includes minimization of costs occur due to operational energy and water consumption together with ownership costs of building materials and building systems. Moreover, a set of penalty functions including equipment capacity, user comfort, CO2 emissions and renewable system payback period are added to the main objective function in the form of constraints to restrict the solution region to user-set design target. Consequently, multi-objective design aims are translated into a single-objective where the penalty functions acts as secondary objectives. The performance of the proposed optimization methodology was evaluated through a case study implementation where different design scenarios were created, optimized and analysed. A hypothetical base-case office building was defined. Three cities located in Turkey namely Istanbul, Ankara and Antalya were selected as building locations. Therefore, the performance of the methodology in different climatic conditions was investigated. An equipment database consists of actual building materials and system equipment commonly used in Turkish construction sector was prepared. In addition, technical and financial data necessary for objective function calculation were collected from the market. The results of the case studies showed that application of the proposed methodology achieved giving climate-appropriate design recommendations, which resulted in major cost reductions and energy savings. One of the most important contributing factors of this thesis is introducing an integrative method where building architectural elements, HVAC system equipment and renewable systems are simultaneously investigated and optimized while interactions between building and systems are being dynamically captured. Moreover, this research is distinctive from previous studies because it makes possible investigating actual market products as energy efficiency design options through its proposed database application and a sub-program that connect optimization engine with the data library. Therefore, application of the methodology can provide support on real-world building design projects and can prevent a mismatch between the optimization recommendations and the available market solutions. Furthermore, another contributing merit of this research is that it achieves formulating competing building design aims in a single objective function, which can still capture multi-dimensions of building design challenge. Global costs are minimized while energy savings are achieved, CO2-equivalent emission is reduced, right-sized equipment are selected, thermal comfort is provided to users and target payback periods of investments are assured. To conclude, the proposed methodology links building energy performance requirements to financial and environmental targets and it provides a promising structure for addressing real life building design challenges through fast and efficient optimization techniques
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