3,528 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
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
Hybrid Ground Vehicle Thermal Management System Using Heat Pipes—Model and Control
The development of Hybrid Electric and Unmanned Ground Vehicles (HEV and UGV) offer various benefits including improved vehicle performance, compatibility with high level control systems, reduced fuel consumption, and less environmental pollution. According to the International Energy Agency (IEA), the number of HEVs and EVs is expected to reach 20 million by the year 2020 (Green Car Congress, 2017). Compared with traditional Internal Combustion (IC) engines, hybrid powertrains are more complicated due to additional electronics including the electric motor, battery pack, and control units. However, these additional components introduce new challenges for the powertrain thermal management system design since they have different operating temperature requirements and modes of heat generation. In a hybrid vehicle, the modes of heat generation, apart from the IC engine, include the electric motor, battery pack, and some electrical subsystems, which lead to a more demanding thermal control system.
A traditional vehicle cooling system is composed of a mechanical water pump, radiator fan(s), hoses, and other mechanical actuators such as a thermostat valve. In recent times, however, computer-controlled actuators such as an electric water pump, variable speed fan(s), and smart valve(s) are being used for higher efficiency and performance. This approach, although effective and efficient for the common IC engine, may pose problems when it comes to the hybrid powertrains owing to limited space, different operating conditions, heat generation rates, etc. In this dissertation, several innovative designs, optimizations, and control strategies using heat pipes in the thermal management system targeted to hybrid powertrain applications will be analyzed.
First, an integrated electric motor air cooling system based on radial heat pipes was designed and the performance was explored through computer simulations. A reduced order electric motor thermal model was introduced to simulate the motor’s internal temperatures. Heat pipes were modeled based on the vapor flow and heat transfer processes, and also selected as the cooling system thermal bus to efficiently remove heat. Mathematical models for the thermal cradle and heat exchanger were developed to complete the cooling system. A series of simulation tests based on the Urban Assault and Convoy Escort driving cycles were used to test the cooling system performance. Numerical results show that the proposed cooling system saves up to 52.1kJ of energy within a 1,800s simulation in comparison to a traditional liquid cooling design (e.g., 67.8% energy saving).
Second, an electric motor liquid hybrid cooling system, for HEV applications, using integrated heat pipes and traditional liquid was designed and simulated. The innovative design features two parallel heat transfer pathways allowing optimal heat removal. Detailed mathematical models were developed for the electric motor, heat pipes, liquid cooling system, and heat exchanger. A classical controller was designed for the heat pipe heat transfer pathway while the liquid cooling pathway was adjusted using a nonlinear controller. Cooling performance was again evaluated based on the Urban Assault driving cycle for various road grades and ambient conditions. Results show that the electric motor temperature can be maintained around the target value of 70°C with 399kJ cooling system energy consumption compared to approximate 770kJ energy consumption with the conventional liquid cooling system (e.g., 48% energy saving).
Third, a smart HEV battery pack thermal management system using heat pipes as a thermal bus to remove heat efficiently was developed. The battery cooling system couples a standard air conditioning (AC) system with traditional ambient air ventilation. A lumped parameter battery thermal model was created to predict the battery core and surface temperatures. A nonlinear model predictive controller (NMPC) was developed to maintain the battery core temperature about the reference value. The system performance and power requirements were investigated for various driving cycles and ambient conditions. Results showed that the proposed thermal management system can maintain the battery core temperature within a small range (maximum tracking error of 2.1°C) using a suitable cooling strategy based on the ambient temperature conditions and battery heat generation rate. Furthermore, the system showed the ability to remove up to 1134.8kJ of heat within the 1200s simulation.
Fourth, a holistic thermal management system for an Unmanned Autonomous Ground Vehicle (UAGV) with a series hybrid powertrain was developed. The use of heat pipes combined with advanced controllers for the vehicle’s electric motors, battery pack, and engine generator set cooling was examined. A series of mathematical models were developed to describe the dynamics and thermal behavior for these elements. Controllers were designed to maintain the components temperatures about their reference values and minimize energy consumption by regulating multiple actuators (e.g., pump, radiator fan, smart valve, blower, and compressor). A vehicle level simulation was conducted which combines the cooling system power consumption with the vehicle power bus. An Urban Assault driving cycle with various road grades and ambient conditions were used for the simulation to show the robustness of the proposed cooling system. Results show that the component temperatures were maintained around their reference values with small errors (2.1°C) and up to 2,955kJ cooling system energy was saved over the 1,800s simulation using heat pipes and the proposed controllers (e.g., 19.8% energy saving).
Overall, this research has developed the basis for the holistic control of HEV powertrain thermal management systems. A suite of model-based advanced controllers was used to simultaneously regulate the cooling actuators for the battery, e-motors, and IC engine. For electronics, heat pipes were introduced to reduce the cooling system energy consumption due to their high effective conductivities. Numerical studies have been conducted using vehicle model under various driving cycle, road grade, and ambient conditions to show the advantages of heat pipes and the proposed controllers. The next generation of thermal management system will feature multiple heat transfer pathways to help reduce energy consumption for a better use of fossil fuel and electric power resources
Optimisation of a high-efficiency solar-driven organic rankine cycle for applications in the built environment
Energy security, pollution and sustainability are major challenges presently facing the international community, in response to which increasing quantities of renewable energy are to be generated in the urban environment. Consequently, recent years have seen a strong increase in the uptake of solar technologies in the building sector. In this work, the potential of a solar combined heat and power (CHP) system based on an organic Rankine cycle (ORC) engine is investigated in a domestic setting. Unlike previous studies that focus on the optimisation of the ORC subsystem, this study performs a complete system optimisation considering both the design parameters of the solar collector array and the ORC engine simultaneously. Firstly, we present thermodynamic models of different collectors, including flat-plate and evacuated-tube designs, coupled to a non-recuperative sub-critical ORC architecture that delivers power and hot water by using thermal energy rejected from the engine. Optimisation of the complete system is first conducted, aimed at identifying operating conditions for which the power output is maximised. Then, hourly dynamic simulations of the optimised system configurations are performed to complete the system sizing. Results are presented of: (i) dynamic 3-D simulations of the solar collectors together with a thermal energy storage tank, and (ii) of an optimisation analysis to identify the most suitable working fluids for the ORC engine, in which the configuration and operational constraints of the collector array are considered. The best performing working fluids (R245fa and R1233zd) are then chosen for a whole-system annual simulation in a southern European climate. The system configuration combining an evacuated-tube collector array and an ORC engine is found to be best-suited for electricity prioritisation, delivering an electrical output of 3,605ÂżkWh/year from a 60Âżm2 collector array. In addition, the system supplies 13,175ÂżkWh/year in the form of domestic hot water, which is equivalent to more than 6 times the average annual household demand. A brief cost analysis and comparison with photovoltaic (PV) systems is also performed, where despite the lower PV investment cost per kWel, the levelised energy costs of the different systems are found to be similar if the economic value of the thermal output is taken into account. Finally, a discussion of the modelled solar-CHP systems results shows how these could be used for real applications and extended to other locationsPeer ReviewedPostprint (updated version
A hybrid modeling approach for steady-state optimal operation of vapor compression refrigeration cycles
This paper presents a steady-state hybrid modeling approach for vapor compression refrigeration cycles which is intended to achieve an optimal system operation from an energy consumption point of view. The model development is based on a static characterization of the main components of the cycle using a hybrid approach, and their integration in a new optimization block. This block allows to determine completely the system stationary state by means of a non-linear optimization procedure subjected to several constraints such as mechanical limitations, component interactions, environmental conditions and cooling load demand. The proposed method has been tested in an experimental pilot plant with good results. Model validation for each identified hybrid model is carried out from a set of experimental data of 82 stationary operating points, with prediction errors below ±10%. The model is also globally validated by comparing experimental and simulated data, with a global mean relative absolute error less than 5%. The basic control structure consists of three decentralized control loops where the controller variables are the secondary fluid temperature at the evaporator inlet, the superheat, and the condenser pressure. While the secondary temperature is assumed as an imposed requirement, the optimal set-points of the other two control loops are searched offline using the proposed refrigerant cycle model. This set-point optimality is defined according to the coefficient of performance for minimizing the total electrical power consumption of the system at steady-state. This energy saving has been confirmed experimentally. The proposed method can be easily adapted for different sets of controlled variables in case of modification of the basic control structure. Furthermore, other energy efficiency metrics can be handily adopted. Considering the tradeoff between the accuracy and computational cost of the hybrid models, the proposed procedure is expected to be used in real-time applications
Using advanced simulation techniques to improve industrial controller’s dependability
Modelica Modeling language is powerful and suitable
for modeling mechatronic systems, being possible to interact
different technological aspects and deal, simultaneously with
different technologies (mechanical, electrical, pneumatic,
hydraulic,..). In this paper it is discussed, in a case study, the
possibility of using this language for modeling an automation
system (controller and plant) in closed loop behavior and also in
defining some parameters of the automation system in order to
optimize some behavior aspects of the system as, for instance,
the time cycle of the automation system. Some aspects relied
with controllers dependability are also discussed and it is
showed how Modelica modeling language can help controllers’
designers improving controllers dependability, when are used
Simulation Techniques
Design, Modeling and Control of a Thermal Management System for Hybrid Electric Vehicles
Hybrid electric vehicle (HEV) technology has evolved in the last two decades to become economically feasible for mass produced automobiles. With the integration of a lithium battery pack and electric motors, HEVs offer a significantly higher fuel efficiency than traditional vehicles that are driven solely by an internal combustion engine. However, the additional HEV components also introduce new challenges for the powertrain thermal management system design. In addition to the common internal combustion engine, the battery pack, the generator(s), as well as the electric motor(s) are now widely applied in the HEVs and have become new heat sources and they also require proper thermal management. Conventional cooling systems have been typically equipped with a belt driven water pump and radiator fan, as well as other mechanical actuators such as the thermostat valve. The operation of these components is generally determined by the engine speed. This open-loop cooling strategy has a low efficiency and suffers the risk of over-cooling the coolant and components within the system. In advanced thermal management systems, the mechanical elements are upgraded by computer controlled actuators including a servo-motor driven pump, variable speed fans, a smart thermostat, and an electric motor driven compressor. These electrified actuators offer the opportunity to improve temperature tracking and reduce parasitic losses. This dissertation investigates a HEV powertrain thermal management system featuring computer controlled cooling system actuators. A suite of mathematical models have been created to describe the thermal behaviour of the HEV powertrain components. Model based controllers were developed for the vehicle\u27s cooling systems including the battery pack, electric motors, and internal combustion engine. Optimal control theory has been applied to determine the ideal battery cooling air temperature and the desired heat removal rate on e-motor cooling surface. A model predictive controller(MPC) was developed to regulate the refrigerant compressor and track the battery cooling air temperature. A series of Lyapunov-based nonlinear controllers have been implemented to regulate the coolant pumps and radiator fans in the cooling systems for the engine and e-motors. Representative numerical results are presented and discussed. Overall, the proposed control strategies have demonstrated the effectiveness in improving both the temperature tracking performance and the cooling system power consumption reduction. The peak temperature error in the selected A123 battery core can be tracked within 0.25 C of the target; a 50% reduction of the vapor compression system energy consumption can be obtained by properly designing the cooling air flow structure. Similarly, the cooling system of HEV electric motors shows that the machine internal peak temperature can be tracked to the target value with a maximum error of 3.9 C and an average error of 0.13 C. A 70% to 81% cooling system energy consumption reduction can be achieved under different driving cycle comparing to classical controller applied to maintain a similar level of hotspot temperature stabilization. The proposed optimal nonlinear controller tracks the engine coolant temperature with an average error of 0.35 C and at least 13% reduction in engine cooling power. Further, a close analysis on the cooling system energy consumption reduction has been conducted with a heat exchanger simulation tool established for cooling system design optimization. This research has developed the basis for the holistic control of HEV powertrain thermal management systems by including a suite of model based nonlinear controllers to simultaneously regulate the cooling actuators for the battery pack, e-motors, and conventional internal combustion engine. Numerical studies has been conducted with a high fidelity HEV model under real driving cycles to demonstrate the advantages of introducing advanced control theory into multi-mode vehicle drive systems
Anti-Idling Systems for Service Vehicles with A/C-R Units: Modeling, Holistic Control, and Experiments
As people have begun to pay more attention to energy conservation and emission reduction in recent
years, anti-idling has become a growing concern for automobile engineers due to the low efficiency
and high emissions caused by engine idling, i.e., the engine is running when the vehicle is not
moving. Currently, different technologies and products have emerged in an effort to minimize engine
idling. By studying and comparing most of these methods, the conclusion can be drawn that there is
still much room to improve existing anti-idling technologies and products. As a result, the optimized
Regenerative Auxiliary Power System (RAPS) is proposed.
Service vehicles usually refer to a class of vehicles that are used for special purposes, such as
public buses, delivery trucks, and long-haul trucks. Among them, there are vehicles with auxiliary
devices such as air conditioning or refrigeration (A/C-R) systems that are essential to be kept running
regardless of the vehicle motion. In addition, such auxiliary systems usually account for a large
portion of fuel from the tank. Food delivery trucks, tourist buses, and cement trucks are examples of
such service vehicles. As a leading contributor to greenhouse gas emissions, these vehicles sometimes
have to frequently idle to for example keep people comfortable, and keep food fresh on loading and
unloading stops. This research is intended to develop and implement a novel RAPS for such service
vehicles with the A/C-R system as the main auxiliary device. The proposed RAPS can not only
electrify the auxiliary systems to achieve anti-idling but also use regenerative braking energy to
power them.
As the main power consuming device, the A/C-R system should be treated carefully in terms of its
efficiency and performance. Thus, the developments of an advanced controller for A/C-R system to
minimize energy consumption and an optimum power management system to maximize the overall
efficiency of the RAPS are the primary objectives of this thesis. In this thesis, a model predictive
controller (MPC) is designed based on a new A/C-R simplified model to minimize the power
consumption while meeting the temperature requirements. The controller is extensively validated
under both common and frosting conditions. Meanwhile, after integrating the RAPS into a service
vehicle, its powertrain turns into a parallel hybrid system due to the addition of an energy storage
system (ESS). For the sake of maximizing the overall efficiency, RAPS requires a power
management controller to determine the power flow between different energy sources. As a result, a
predictive power management controller is developed to achieve this objective, where a regenerative
iv
braking control strategy is developed to meet the driver’s braking demand while recovering the
maximum braking energy when vehicles brake. For the implementation of the above controllers, a
holistic controller of the RAPS is designed to deal with the auxiliary power minimization and power
management simultaneously so as to maximize the overall energy efficiency and meet the high
nonlinearities and wide operating conditions
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