Fuel Efficient Balance of Plant and Power Split Control Strategies for Fuel Cell Vehicles

Fuel cell (FC) systems with on-board hydrogen storage offer long range, fast refueling, with low audible and thermal signatures. These attributes make fuel cell vehicles (FCVs) the best option for fleet vehicles with high utilization and stringent environmental requirements. A FC system consists of the stack, which performs the electrical conversion of hydrogen to electricity, the balance of plant (BOP) components including pumps, ejectors, and blowers which are responsible for supplying reactants (hydrogen and air) at the correct rates, humidification, and thermal management hardware. Fuel cells (FCs) are typically hybridized with a battery to recuperate the braking energy and improve the system durability by reducing the a) transient and high current spikes, b) idling conditions with high open circuit potential, and c) the number of startup and shutdown cycles. At the vehicle level satisfying driver's torque/power demand is achieved by choosing the power split between the fuel cell and battery. Low hydrogen consumption and vehicle efficiency can be achieved through load preview and simultaneous optimization of vehicle speed and power split to regulate battery state of charge and fuel cell thermal management as this thesis shows. This dissertation presents control strategies to address the above challenges for different size and weight of fuel cell vehicles motivated by the diversity of powertrains managed in the defense industry. Air-cooled stacks are considered for small power systems such as ground robots. To this end, an air-cooled fuel cell system model with a fan as a BOP component is considered. The optimization of the lumped thermal dynamic addresses the FC bulk temperature taking into account the parasitic loss of the electric fan that supplies air for the reaction and cooling simultaneously. We analyze prior work that used an offline numerical optimization method called General Purpose Optimal Control Software (GPOPS) to solve the optimal fan flow and fuel cell current for this combined BOP and powersplit optimization strategy. We show that the optimal FC temperature and current setpoints depend on the drive cycle, but their values does not change substantially within the cycle. Given the intra-cycle invariance of the setpoints, we develop two proportional-integral (PI) controllers to achieve the power split and the BOP. Secondly, a large fuel cell vehicle (FCV) with multiple kW of power uses a liquid cooling hardware strategy and imposes low parasitic losses hence the optimization emphasis shifts on the power split strategy. A dynamic programming and equivalent minimization consumption control strategies are developed and compared for different battery sizes and battery cell chemistries. Thirdly, co-optimization and sequential optimization of the velocity profile and the power split were compared and developed for a liquid-cooled FC with battery for a Small Multipurpose Equipment Transport (SMET) vehicle in terms of energy consumption, operating modes, and computational cost. Last but not least, the importance of accurate SOC estimation for the battery utilization is addressed by combining voltage and force measurements to improve SOC estimation for an efficient, scalable, and safe fuel cell vehicle system.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169736/1/miriamaf_1.pd

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

Ultracapacitor Heavy Hybrid Vehicle: Model Predictive Control Using Future Information to Improve Fuel Consumption

This research is concerned with the improvement in the fuel economy of heavy transport vehicles through the use of high power ultracapacitors in a mild hybrid electric vehicle platform. Previous work has shown the potential for up to 15% improvement on a hybrid SUV platform, but preliminary simulations have shown the potential improvement for larger vehicles is much higher. Based on vehicle modeling information from the high fidelity, forward-looking modeling and simulation program Powertrain Systems Analysis Toolkit (PSAT), a mild parallel heavy ultracapacitor hybrid electric vehicle model is developed and validated to known vehicle performance measures. The vehicle is hybridized using a 75kW motor and small energy storage ultracapacitor pack of 56 Farads at 145 Volts. Among all hybridizing energy storage technologies, ultracapacitors pack extraordinary power capability, cycle lifetime, and ruggedness and as such are well suited to reducing the large power transients of a heavy vehicle. The control challenge is to effectively manage the very small energy buffer (a few hundred Watt-hours) the ultracapacitors provide to maximize the potential fuel economy. The optimal control technique of Dynamic Programming is first used on the vehicle model to obtain the \u27best possible\u27 fuel economy for the vehicle over the driving cycles. A variety of energy storage parameters are investigated to aid in determining the best ultracapacitor system characteristics and the resulting effects this has on the fuel economy. On a real vehicle, the Dynamic Programming method is not very useful since it is computationally demanding and requires predetermined vehicle torque demands to carry out the optimization. The Model Predictive Control (MPC) method is an optimization-based receding horizon control strategy which has shown potential as a powertrain control strategy in hybrid vehicles. An MPC strategy is developed for the hybrid vehicle based on an exponential decay torque prediction method which can achieve near-optimal fuel consumption even for very short prediction horizon lengths of a few seconds. A critical part of the MPC method which can greatly affect the overall control performance is that of the prediction model. The use of telematic based \u27future information\u27 to aid in the MPC prediction method is also investigated. Three types of future information currently obtainable from vehicle telematic technologies are speed limits, traffic conditions, and traffic signals, all of which have been incorporated to improve the vehicle fuel economy

Design and Evaluation of Hybrid Energy Storage Systems for Electric Powertrains

At the time of this writing, increasing pressure for fuel efficient passenger vehicles has prompted automotive manufactures to invest in the research and development of electrically propelled vehicles. This includes vehicles of strictly electric drive and hybrid electric vehicles with internal combustion engines. To investigate some of the many technological innovations possible with electric power trains, the AUTO21 network of centres of excellence funded project E301-EHV; a project to convert a Chrysler Pacifica into a hybrid electric vehicle. The converted vehicle is intended for use as a test-bed in the research and development of a variety of advances pertaining to electric propulsion. Among these advances is hybrid energy storage, the focus of this investigation. A key difficulty of electric propulsion is the portable storage or provision of electricity, challenges are twofold; (1) achieving sufficient energy capacity for long distance driving and (2) ample power delivery to sustain peak driving demands. Where gasoline is highly energy dense and may be burned at nearly any rate, storing large quantities of electrical energy and supplying it at high rate prove difficult. Furthermore, the demands of regenerative braking require the storage system to undergo frequent current reversals, reducing the service life of some electric storage systems. A given device may be optimized for one of either energy storage or power delivery, at the sacrifice of the other. A hybrid energy storage system (HESS) attempts to address the storage needs of electric vehicles by combining two of the most popular storage technologies; lithium ion batteries, ideal for high energy capacity, and ultracapacitors, ideal for high power discharge and frequent cycles. Two types of HESS are investigated in this study; one using energy-dense lithium ion batteries paired with ultracapacitors and the other using energy-dense lithium ion batteries paired with ultra high powered batteries. These two systems are compared against a control system using only batteries. Three sizes of each system are specified with equal volume in each size. They are compared for energy storage, energy efficiency, vehicle range, mass and relative demand fluctuation when simulated for powering a model Pacifica through each of five different drive cycles. It is shown that both types of HESS reduce vehicle mass and demand fluctuation compared to the control. Both systems have reduced energy efficiency. In spite of this, a battery-battery system increases range with greater storage capacity, but battery-capacitor systems have reduced range. It is suggested that further work be conducted to both optimize the design of the hybrid storage systems, and improve the control scheme allocating power demand across the two energy sources

Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered

Operating cycle representations for road vehicles

This thesis discusses different ways to represent road transport operations mathematically. The intention is to make more realistic predictions of longitudinal performance measures for road vehicles, such as the CO2 emissions. It is argued that a driver and vehicle independent description of relevant transport operations increase the chance that a predicted measure later coincides with the actual measure from the vehicle in its real-world application. This allows for fair comparisons between vehicle designs and, by extension, effective product development. Three different levels of representation are introduced, each with its own purpose and application. The first representation, called the bird\u27s eye view, is a broad, high-level description with few details. It can be used to give a rough picture of the collection of all transport operations that a vehicle executes during its lifetime. It is primarily useful as a classification system to compare different applications and assess their similarity. The second representation, called the stochastic operating cycle (sOC) format, is a statistical, mid-level description with a moderate amount of detail. It can be used to give a comprehensive statistical picture of transport operations, either individually or as a collection. It is primarily useful to measure and reproduce variation in operating conditions, as it describes the physical properties of the road as stochastic processes subject to a hierarchical structure.The third representation, called the deterministic operating cycle (dOC) format, is a physical, low-level description with a great amount of detail. It describes individual operations and contains information about the road, the weather, the traffic and the mission. It is primarily useful as input to dynamic simulations of longitudinal vehicle dynamics.Furthermore, it is discussed how to build a modular, dynamic simulation model that can use data from the dOC format to predict energy usage. At the top level, the complete model has individual modules for the operating cycle, the driver and the vehicle. These share information only through the same interfaces as in reality but have no components in common otherwise and can therefore be modelled separately. Implementations are briefly presented for each module, after which the complete model is showcased in a numerical example.The thesis ends with a discussion, some conclusions, and an outlook on possible ways to continue

Fuel Consumption Reduction Through Velocity Optimization for Light-Duty Autonomous Vehicles with Different Energy Sources

The emergence of self-driving cars provides an additional flexibility to the vehicle controller, by eliminating the driver and allowing for control of the vehicle's velocity. This work employs constrained optimal control techniques with preview of position constraints, to derive optimal velocity trajectories in a longitudinal vehicle following mode. A framework is developed to compare autonomous driving to human driving, i.e. the Federal Test Procedures of the US Environmental Protection Agency. With just velocity smoothing, improvements by offline global optimization of up to 18% in Fuel Economy (FE), are shown for certain drive cycles in a baseline gasoline vehicle. Applying the same problem structure in an online optimal controller with 1.5 s preview showed a 12% improvement in FE. This work is further extended by using a lead velocity prediction algorithm that provides inaccurate future constraints. For a 10 s prediction horizon, a 10% improvement in FE has been shown. A more conventional procedure for achieving velocity optimization would be the minimization of energy demand at the wheels. This method involves a non-linear model thus increasing optimization complexity and also requires additional information about the vehicle such as mass and drag coefficients. It is shown that even though tractive energy minimization has a lower energy demand than velocity smoothing, smoothing works as well if not better when it comes to reducing fuel consumption. These results are shown to be valid in simulation across three different engines ranging from 1.2 L-turbocharged to 4.3 L-naturally aspirated. The implication of these results is that tractive energy minimization requiring more complex control does not work well for conventional gasoline vehicles. It is further shown that using reduced order powertrain models currently found in literature for velocity optimization, can result in worse FE than previous optimizations. Therefore, an easily implementable, vehicle agnostic velocity smoothing algorithm could be preferred for drive cycle optimization. Employing these same velocity optimization techniques for a battery electric vehicle (BEV) can increase battery range by 15%. It is further demonstrated that eco-driving and regenerative braking are not complimentary and eco-driving is always preferred. Finally, power split optimization has been carried out for a fuel cell hybrid, and it has been shown that a rule-based strategy with drive cycle preview could match the global optimal results.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149826/1/niketpr_1.pd

Aeronautical engineering: A continuing bibliography with indexes (supplement 255)

This bibliography lists 529 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

INTER-ENG 2020

These proceedings contain research papers that were accepted for presentation at the 14th International Conference Inter-Eng 2020 ,Interdisciplinarity in Engineering, which was held on 8–9 October 2020, in Târgu Mureș, Romania. It is a leading international professional and scientific forum for engineers and scientists to present research works, contributions, and recent developments, as well as current practices in engineering, which is falling into a tradition of important scientific events occurring at Faculty of Engineering and Information Technology in the George Emil Palade University of Medicine, Pharmacy Science, and Technology of Târgu Mures, Romania. The Inter-Eng conference started from the observation that in the 21st century, the era of high technology, without new approaches in research, we cannot speak of a harmonious society. The theme of the conference, proposing a new approach related to Industry 4.0, was the development of a new generation of smart factories based on the manufacturing and assembly process digitalization, related to advanced manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, and manufacturing tools and equipment. The conference slogan was “Europe’s future is digital: a broad vision of the Industry 4.0 concept beyond direct manufacturing in the company”