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

Investigation of Advanced Engine Cooling Systems - Optimization and Nonlinear Control

Advanced automotive engine cooling systems can positively impact the performance, fuel economy, and reliability of internal combustion engines. A smart engine cooling system typically features multiple real time computer controlled actuators: a three way linear smart valve, a variable speed coolant pump, and electric radiator fan(s). In this dissertation, several innovative comprehensive nonlinear control and optimization operation strategies for the next generation smart cooling application will be analyzed. First, the optimal control has been investigated to minimize the electric energy usage of radiator fan matrix. A detailed mathematical model of the radiator fan(s) matrix operation and the forced convection heat transfer process was developed to establish a mixed integer nonlinear programming problem. An interior points approach was introduced to solve the energy consumption minimization problem. A series of laboratory tests have been conducted with different fan configurations and rotational shaft speed combinations, with the objective to cool a thermal loaded engine. Both the mathematical approach and the laboratory test results demonstrated the effectiveness of similar control strategies. Based on the tests data and mathematical analysis, an optimization control strategy reduced the fan matrix power consumption by up to 67%. Second, a series of experimental laboratory tests were implemented to investigate the contributions of each electro-mechanical device in automotive thermal management system. The test results established a basis for several key operating conclusions. The smart valve and variable speed pump impacted the engine temperature by adjusting the heat transfer rate between the engine and the radiator through coolant redirection and/or coolant flow rate. On the other hand, the radiator fan(s) operation affects the engine\u27s temperature by modifying the heat rejection rate of the radiator which can influence the entire cooling system. In addition, the smart valve\u27s operation changes the engine\u27s temperature magnitude the greatest amount followed by the radiator fan(s) and the coolant pump. Furthermore, from a power consumption aspect, the radiator fan(s) consumes the most engine power in comparison to the two other actuators. Third, a Lyapunov based nonlinear control strategy for the radiator fan matrix was studied to accommodate transient engine temperature tracking at heavy heat load. A reduced order mathematical model established a basis for the closed-loop real time feedback system. Representative numerical and experimental tests demonstrated that the advanced control strategy can regulate the engine temperature tracking error within 0.12°C and compensate the unknown heat load. The nonlinear controller provided superior performance in terms of power consumption and temperature tracking as evident by the reduced magnitude when compared to a classical proportional integral with lookup table based controller and a bang bang controller. Fourth, a nonlinear adaptive multiple-input and multiple-output (NAMIMO) controller to operate the smart valve and radiator fans has been presented. This controller regulates the engine temperature while compensating for unknown wide range heat loads and ram air effects. A nonlinear adaptive backstepping (NAB) control strategy and a state flow (SF) control law were introduced for comparisons. The test results indicated that the NAMIMO successfully regulated the engine temperature to a desired value (tracking error, |e|\u3c0.5°C, at steady state) subject to various working conditions. In contrast, the NAB control law consumes the least radiator fan power but demonstrated a larger average temperature tracking error (40% greater than the NAMIMO controller), a longer response time (34% greater than the NAMIMO controller), and defected when the heat load was low. Lastly, the SF controller, characterized by greater oscillation and electrical power consumption (18.9% greater than the NAMIMO controller), was easy to realize and maintained the engine temperature to within |e|\u3c5°C. An important aspect of engineering research is the knowledge gained from learning materials to fully understand the thermal management. As part of the dissertation, advanced three-dimensional (3D) visualization and virtual reality (VR) technology based engineering education methods has been studied. A series of computer aided design (CAD) models with storyboards have been created to provide a step to step guide for developing the learning modules. The topics include automotive, aerospace, and manufacturing. The center for aviation and automotive technological education using virtual e-schools (CA2VES) at Clemson University has developed a comprehensive e-learning system integrated with eBooks, mini video lectures, 3D virtual reality technologies, and online assessments as supplementary materials to engineering education

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

MECHATRONIC SYSTEM DESIGN - A HYDRAULIC-BASED ENGINE COOLING SYSTEM DESIGN AND REFINEMENT OF A TECHNICAL ELECTIVE MECHATRONICS COURSE

The improvement of consumer products and industrial processes, in terms of functionality and reliability, has recently focused on the integration of sensors and real time controllers with attached actuators into the given physical system. The likelihood of long-term market penetration of smart devices has placed an emphasis on preparing engineering graduates for technology leadership roles in the workforce. This thesis examines mechatronic systems in two manners. First, an intelligent automotive internal combustion engine cooling system is studied for ground vehicles using hydraulic actuators which offer the opportunity for greater versatility and performance. Second, improvements to a technical elective mechatronics course at Clemson University in the Department of Mechanical Engineering have been completed to offer a better educational experience for both undergraduate and graduate students. Traditional and modern internal combustion engine cooling systems typically use a mechanical wax based thermostat along with a number of mechanical and/or electric actuators to remove the excessive heat of combustion from the engine block. The cooling system\u27s main objective is to maintain the engine temperature within a prescribed range which optimizes engine performance and promotes mechanical longevity. However, the cooling system adds to parasitic engine losses and vehicle weight, so a mechatronic based smart thermal management system has been designed to explore the higher power density and controllability of hydraulic actuators. In this research project, the experimental data has been initially gathered using a 4.6L gasoline engine with a mechanical wax based thermostat valve, engine driven coolant pump, and a hydraulic motor driven radiator fan with classical feedback control. A series of mathematical models for the hydraulic, electric, and thermal automotive subsystems have been developed to estimate the engine, coolant, and radiator temperatures as well as the overall system performance for various operating conditions. The experimental test platform features a medium duty eight cylinder internal combustion engine, stand-alone radiator, engine dynamometer, smart cooling system components, high speed data acquisition system, and real-time control algorithm with associated sensors. Specifically, J-type and K-type thermocouples measure the engine block, coolant, and radiator core temperatures at various locations. A multiplexer switches these input signals at predetermined intervals to accommodate the large number of temperature probes. Further, optical sensors measure the engine and radiator fan speeds, and pressure sensors record the hydraulic line pressures. A hydraulic direction control valve was used to adjust the speed of the radiator fan. The experimentally recorded engine data was compared with the numerical simulation results to estimate the engine\u27s thermal behavior for warm up and idle conditions. The findings demonstrated that the proposed experimental model and mathematical models successfully controlled the engine temperature within ±1.5°K . In the future, the mathematical models can be used for linear quadratic regulator and Lyapunov-based nonlinear controllers after further refinement and the addition of state variables for the engine thermal management system. To implement such a mechatronic-based cooling system, engineers must have a fundamental understanding of system dynamics, control theory, instrumentation, and system integration concepts. Given the growing industrial demand for graduates with diverse engineering knowledge, a mechatronic systems course has been designed in the Department of Mechanical Engineering at Clemson University. This mechatronics course, ME 417/617, has been designed to introduce both engineering and personal skills. The students, who would successfully complete the course, will be able to join global work teams designing smart products. The course uses various teaching paradigms such as classroom activities, laboratory experiments, team based design projects, and plant tours to introduce the concepts and offer hands-on experience. As part of a continuous improvement process, the course has been evaluated using assessment methods such as pre- and post-tests, qualitative measures, and advisory panel observations. Over a four course offering period (2008-2011), the pre- and post-tests reflect improvements in the students\u27 personal growth (7.0%), team building (12.8%), mechanics/engineering (25.4%), and human factor (17%) skills. The qualitative assessment was completed using student feedback regarding the course content. Most of the students reported that they liked the course and its \u27hands-on\u27 experimental approach. An advisory panel, consisting of industry experts, course instructors, and faculty analyzed the progress of students and evaluated the course materials. The advisory panel\u27s recommendations established the direction for continuous improvements to successfully teach the concepts of mechatronics and better meet the student needs. Going forwards, the mechatronic systems course will serve an important role in preparing graduates for future endeavors

PREDICTIVE CONTROL OF POWER GRID-CONNECTED ENERGY SYSTEMS BASED ON ENERGY AND EXERGY METRICS

Building and transportation sectors account for 41% and 27% of total energy consumption in the US, respectively. Designing smart controllers for Heating, Ventilation and Air-Conditioning (HVAC) systems and Internal Combustion Engines (ICEs) can play a key role in reducing energy consumption. Exergy or availability is based on the First and Second Laws of Thermodynamics and is a more precise metric to evaluate energy systems including HVAC and ICE systems. This dissertation centers on development of exergy models and design of model-based controllers based on exergy and energy metrics for grid-connected energy systems including HVAC and ICEs. In this PhD dissertation, effectiveness of smart controllers such as Model Predictive Controller (MPC) for HVAC system in reducing energy consumption in buildings has been shown. Given the unknown and varying behavior of buildings parameters, this dissertation proposes a modeling framework for online estimation of states and unknown parameters. This method leads to a Parameter Adaptive Building (PAB) model which is used for MPC. Exergy destruction/loss in a system or process indicates the loss of work potential. In this dissertation, exergy destruction is formulated as the cost function for MPC problem. Compared to RBC, exergy-based MPC achieve 22% reduction in exergy destruction and 36% reduction in electrical energy consumption by HVAC system. In addition, the results show that exergy-based MPC outperforms energy-based MPC by 12% less energy consumption. Furthermore, the similar exergy-based approach for building is developed to control ICE operation. A detailed ICE exergy model is developed for a single cylinder engine. Then, an optimal control method based on the exergy model of the ICE is introduced for transient and steady state operations of the ICE. The proposed exergy-based controller can be applied for two applications including (i) automotive (ii) Combined Heat and Power (CHP) systems to produce electric power and thermal energy for heating purposes in buildings. The results show that using the exergy-based optimal control strategy leads to an average of 6.7% fuel saving and 8.3% exergy saving compared to commonly used FLT based combustion control. After developing thermal and exergy models for building and ICE testbeds, a framework is proposed for bilevel optimization in a system of commercial buildings integrated to smart distribution grid. The proposed framework optimizes the operation of both entities involved in the building-to-grid (B2G) integration. The framework achieves two objectives: (i) increases load penetration by maximizing the distribution system load factor and (ii) reduces energy cost for the buildings. The results show that this framework reduces commercial buildings electricity cost by 25% compared to the unoptimized case, while improving the system load factor up to 17%

Multiple Heat Exchanger Cooling System for Automotive Applications – Design, Mathematical Modeling, and Experimental Observations

The design of the automotive cooling systems has slowly evolved from engine-driven mechanical to computer-controlled electro-mechanical components. With the addition of computer-controlled variable speed actuators, cooling system architectures have been updated to maximize performance and efficiency. By switching from one large radiator to multiple smaller radiators with individual flow control valves, the heat rejection requirements may be precisely adjusted. The combination of computer regulated thermal management system should reduce power consumption while satisfying temperature control objectives. This research focuses on developing and analyzing a multi-radiator system architecture for implementation in ground transportation applications. The premise is to use a single radiator during low thermal loads and activate the second radiator during high thermal loading scenarios. Ground vehicles frequently use different radiators for each component that needs cooling (e.g., engine blocks, electronics, and motors) since they have different optimal working temperatures. The use of numerous smaller heat exchangers adds more energy-management features and alternative routes for carrying on with operation in the event of a crucial subsystem failure. Moreover, despite cooling systems being designed for maximum thermal loads, most vehicles typically operate at a small fraction of their peak values. To study and examine the planned multi-heat exchanger cooling system concepts, various computer simulations and experimental tests were performed. A nonlinear state space model, featuring input and output heat flow paradigms, was developed using a multi-node resistance-capacitance thermal model. The heat removal rate from the radiator(s) was estimated using the -NTU method as downstream fluid temperatures were not required. The system performance was studied for two driving cycles proposed by the Environmental Protection Agency (EPA) – urban and highway driving schedules. The computer simulation was validated using the laboratory setup in the High Bay Area of Fluor Daniel Engineering Innovation Building. The configuration features computer controlled variable speed electric motor driven coolant pump and independent variable speed fans for each radiator to provide desired fluid flow rates. The pump and fan power consumptions are approximately 0.8-1.2 kW and 0.4-3.2 kW, which corresponds to coolant and air flow rates of 0.2-1.5 kg/s and 0.5-1.75 kg/s, respectively. Two servo motor-controlled gate valves limit the coolant outlet from each radiator. Various thermocouples and a magnetic flow sensor record test data in real time using a dSpace DS1103 data acquisition control system. Designing and analyzing a nonlinear control architecture for the suggested system was the last phase in the study process. A nonlinear controller equipped TMS should offer higher energy efficiency and overall system performance. Three controllers—sliding mode, stateflow, and classical—were designed and implemented in Matlab/Simulink and placed onto the dSpace hardware. The sliding mode controller is recommended for high performance applications since it offers steady temperature tracking, 5oC, an acceptable response time, 120 sec, but suffers from frequent changes in fan speed. The stateflow controller exhibited the fewest fan speed oscillations, the fastest response time, 88 sec, and the smallest temperature offset, 3oC, it is advised for use in common passenger vehicle applications. Both controllers need around six minutes to warm up. The traditional controller, meanwhile, had the quickest warmup, 600 sec, but the slowest response time, 215 sec. Nonlinear cooling systems are essential for maintaining component temperatures which will enable vehicle reliability, and maximize performance given the focus on hybrid and electric vehicles

Innovative Thermal Management Systems for Autonomous Vehicles — Design, Model, and Test

Emphasis on reducing fossil fuel consumption and greenhouse gas emissions, besides the demand for autonomy in vehicles, made governments and automotive industries move towards electrification. The integration of an electric motor with battery packs and on-board electronics has created new thermal challenges due to the heat loads\u27 operating conditions, design configurations, and heat generation rates. This paradigm shift necessitates an innovative thermal management system that can accommodate low, moderate, and high heat dissipations with minimal electrical or mechanical power requirements. This dissertation proposes an advanced hybrid cooling system featuring passive and active cooling solutions in a thermal bus configuration. The main purpose is to maintain the heat loads’ operating temperatures with zero to minimum power requirements and improved packaging, durability, and reliability. In many operating instances, a passive approach may be adequate to remove heat from the thermal source (e.g., electric motor) while a heavy load would demand both the passive and active cooling systems operate together for reduced electric power consumption. Further, in the event of a failure (e.g., coolant hose leak, radiator tube leak) in the conventional system, the passive system offers a redundant operating mode for continued operation at reduced loads. Besides, the minimization of required convective heat transfer (e.g., ram air effect) about the components for supplemental cooling enables creative vehicle component placement options and optimizations. Throughout this research, several cooling system architectures are introduced for electric vehicle thermal management. Each design is followed by a mathematical model that evaluates the steady-state and transient thermal responses of the integrated heat load(s) and the developed cooling system. The designs and the mathematical models are then validated through a series of thermal tests for a variety of driving cycles. Then, the cooling system design configuration is optimized using the validated mathematical model for a particular application. The nonlinear optimization study demonstrates that a 50\% mass reduction could be achieved for a continuous 12kW heat-dissipating demand while the electric motor operating temperature has remained below 65 centigrade degrees. Next, several real-time controllers are designed to engage the active cooling system for precise, stable, and predictable temperature regulation of the electric motor and reduced power consumption. A complete experimental setup compares the controllers in the laboratory’s environment. The experimental results indicate that the nonlinear model predictive control reduces the fan power consumption by 73% for a 5% increase in the pump power usage compared to classical control for a specific 60-minute driving cycle. In conclusion, the conducted experimental and numerical studies demonstrate that the proposed hybrid cooling strategy is an effective solution for the next generation of electrified civilian and combat ground vehicles. It significantly reduces the reliance on fossil fuels and increases vehicle range and safety while offering a silent mode of operation. Future work is to implement the developed hybrid cooling system on an actual electric vehicle, validate the design, and identify challenges on the road

A predictive dynamic model of a smart cogeneration plant fuelled with fast pyrolysis bio-oil

Small scale biomass-based cogeneration has the potential to contribute significantly to a clean, flexible, secure, and cost-efficient energy system. It provides flexibility to future energy systems by balancing variable intermittent renewable energy sources. To exploit its flexibility, a smart control unit is needed. To enable smart control of a cogeneration unit, and to determine its optimal working points, a dynamic system model is required. The purpose of this study is to develop, parameterize and tune a dynamic model of a cogeneration plant fuelled with fast pyrolysis bio-oil. The system is a hybrid diesel generator/flue gas boiler plant for electricity generation and water/space heating. The plant has two unique features: (i) pyrolysis bio-oil is a new fuel for both engine and boiler, and as such it influences their operation and emissions, (ii) power and heat generation are partially decoupled hence non-linearly correlated. The paper presents the integration of the components’ dynamic models into a system model. The model is parameterized and partially validated using measurements from a turbocharged four-cylinder diesel engine and a swirl burner both running on FPBO. Preliminary controls are designed and evaluated. Results show applicability and usefulness of the model for cogeneration system analysis and control design evaluation

Thermal management model for a plug-in hybrid electric vehicle

L’elettrificazione dei veicoli diventerà sempre più preminente sia per ridurre i consumi sia per soddisfare le sempre più stringenti normative sulle emissioni. L' aggiunta di nuovi componenti, cioè motori elettrici, inverter e batteria ad alto voltaggio, permette di aumentare la massima coppia disponibile alle ruote e l’energia immagazzinata a bordo, ma aumenta anche il peso della vettura. Inoltre, questi componenti, pur avendo una efficienza molto elevata, producono una rilevante quantità di calore che deve essere opportunamente rimossa. Al fine di garantire efficienza e affidabilità dell’intero sistema veicolo, l’impianto di raffreddamento deve essere riprogettato. Lo sviluppo di un modello termico può certamente aiutare a progettare al meglio il completo sistema di gestione e controllo della temperatura, visti i molteplici aspetti da considerare. Il veicolo considerato nel presente lavoro di tesi ha un’architettura ibrida P1-P4 e comprende tre circuiti di raffreddamento tra loro separati. Il modello permette di conoscere portata, pressioni e temperature del refrigerante. In primo luogo, la parte idraulica è stata modellata, comprensiva di curva caratteristica della pompa e perdite di carico. In secondo luogo, è stata inclusa la descrizione termica. L’obiettivo principale del presente lavoro è quello di costruire un ambiente in cui successivamente sviluppare strategie di controllo di gestione termica. Gli input del modello sono principalmente parametri legati al powertrain (coppia e velocità di rotazione del motore termico ed elettrico) più i segnali di controllo (per pompe elettriche, ventilatori e compressore) mentre gli output sono la descrizione idraulica e termica del refrigerante nei tre diversi circuiti, più le temperature di batteria e motori elettrici. Il modello costruito è stato poi validato, basandosi sui dati sperimentali a disposizione

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme