Industrial control systems (ICS) play an extremely important role in the world around us. They have helped in reducing human effort and contributed to automation of processes in oil refining, power generation, food and beverage and production lines. With advancement in technology, embedded platforms have emerged as ideal platforms for implementation of such ICSes. Traditional approaches in ICS design involve switching from a model or modeling environment directly to a real-world implementation. Errors have the potential to go unnoticed in the modeling environment and have a tendency to affect real control systems. Current models for error identification are complex and affect the design process of ICS appreciably. This thesis adds an additional layer to ICS design: an Interface Abstraction Process (IAP). IAP helps in incremental migration from a modeling environment to a real physical environment by supporting intermediate design versions. Implementation of the IAP is simple and independent of control system complexity. Early error identification is possible since intermediate versions are supported. Existing control system designs can be modified minimally to facilitate the addition of an extra layer. The overhead of adding the IAP is measured and analysed. With early validation, actual behavior of the ICS in the real physical setting matches the expected behavior in the modeling environment. This approach to ICS design adds a significant amount of latency to existing ICSes without affecting the design process significantly. Since the IAP helps in early design validation, it can be removed before deployment in the real-world. / Master of Science

When building aircrafts, structural components must be designed for high strength, low cost, and easy assembly.To meet these conditions structural components are often based upon previous designs, even if a new component is developed.Refining previous designs can be a good way of preserving knowledge but can also limit the exploration of new design concepts. Currently the design process for structural aircraft components at SAAB is managed by design engineers. The design engineer is responsible for ensuring the design meets requirements from several different disciplines such as structural analysis, manufacturing, tool design, and assembly.Therefore, the design engineer needs to have good communication with all disciplines and an effective flow of information. The previous design is refined, it is then reviewed and approved by adjacent disciplines.Reviewing designs is an iterative process, and when several disciplines are involved it quickly becomes time consuming.Any time the design is altered it has to be reviewed once more by all disciplines to ensure the change is acceptable.So there is a need for further customizing the design concept to decrease the number of iterations when reviewing. Design Space Exploration DSE is a well known method to explore design alternatives before implementation and is used to find new concepts.This thesis investigates if DSE can be used to facilitate the design process of structural aircraft components and if it can support the flow of information between different disciplines.To find a suitable discipline to connect with design a prestudy is conducted, investigating what information affect structural design and how it is managed.The information flow is concluded in a schematic diagram where structural analysis is chosen as additional discipline. By using topology optimization in a DSE, design and structural analysis are connected.The design space can be explored with regards to structural constraints.The thesis highlights the possibilities of using DSE with topology optimization for developing structural components and proposes a method for including it in the design process.

Hydrogen fuel cell technology has emerged as an efficient and clean alternative to internal combustion engines for powering vehicles, and hydrogen powertrains will aid in addressing key environmental issues such as urban air quality and global warming. This work demonstrates the effectiveness of a „hardware-in-loop‟ (HIL) simulation system for validating the safety and effectiveness of control algorithms for a hydrogen fuel cell hybrid passenger vehicle. A significant amount of the work completed in conjunction with the thesis topic was the design and construction of the fuel cell vehicle for competition. Producing a „rolling test bench‟ that generates data to be used to create HIL simulation models required nearly two years of work before an acceptable level of reliability was reached to produce usable data. Some detail will be given in this thesis regarding the infrastructure modifications required to safely build a hydrogen fuel cell vehicle, as well as the design challenges faced in the integration of a fuel cell power module, two electric drive motors, a nickel metal hydride battery, and required power electronics into a small sport utility vehicle originally designed for an internal combustion powertrain. The virtual control validation performed involved designing dynamic models of the systems of interest and performing real-time simulation to ensure that the appropriate controller response is observed. For this thesis, emphasis was placed on several key vehicle control topics. Communication robustness was evaluated to ensure that the complicated vehicle communication network could effectively handle traffic from the six powertrain sub-controllers. Safety algorithms were tested for appropriate response to fault conditions. Control systems were developed and tuned offline reducing the amount of time required for in-vehicle development and testing. Software-in-the-loop simulation was used to check initial code integrity and to validate the hardware-in-the-loop vehicle models. The methodology presented in this work was found to be sufficient for a thorough safety and rationality evaluation of control strategies for hybrid fuel cell vehicles.

Vehicle design and control is an attractive area of research in that it embodies a convergence of societal need, technical limitation, and emerging capability. Environmental, political, and monetary concerns are driving the automotive industry towards sustainable transportation, manifested as increasing powertrain electrification in a gradual transition to fossil-free energy vectors. From an electrochemical degradation and control systems perspective, this transition introduces significant technical uncertainty. Initial indications are that the initial battery designs will have twice the required capacity due to degradation concerns. As the battery is a major contributor to the cost of these vehicles the over-sizing represents a significant threat to the ability of OEMs to produce cost-competitive vehicles. This potential barrier is further amplified when the combustion engine is removed and battery-electric or fuel-cell hybrid vehicles are considered. This thesis researches the application of model-based design for optimal design of fuel cell hybrid powertrains considering power source degradation. The intent is to develop and evaluate tools that can determine the optimal sizing and control of the powertrain; reducing the amount of over-sizing by numerically optimization rather than a sub-optimal heuristic design. A baseline hybrid fuel cell vehicle model is developed and validated to a hybrid fuel cell SUV designed and built at the University of Waterloo. Lithium-ion battery degradation models are developed and validated to data captured off a hybrid powertrain test stand built as part of this research. A fuel cell degradation model is developed and integrated into the vehicle model. Lifetime performance is modeled for four hybrid control strategies, demonstrating a significant impact of the hybrid control strategy on powertrain degradation. A plug-in variation of the architecture is developed. The capacity degradation of the battery is found to be more significant than the power degradation. Blended and All-electric charge-depleting hybrid control strategies are integrated and lifetime performance is simulated. The blended charge-depleting control strategy demonstrated significantly less degradation than the all-electric strategy. An oversized battery is integrated into the vehicle model and the benefit of oversizing on reducing the battery degradation rate is demonstrated.

Hydrogen fuel cell technology has emerged as an efficient and clean alternative to internal combustion engines for powering vehicles, and hydrogen powertrains will aid in addressing key environmental issues such as urban air quality and global warming. This work demonstrates the effectiveness of a „hardware-in-loop‟ (HIL) simulation system for validating the safety and effectiveness of control algorithms for a hydrogen fuel cell hybrid passenger vehicle. A significant amount of the work completed in conjunction with the thesis topic was the design and construction of the fuel cell vehicle for competition. Producing a „rolling test bench‟ that generates data to be used to create HIL simulation models required nearly two years of work before an acceptable level of reliability was reached to produce usable data. Some detail will be given in this thesis regarding the infrastructure modifications required to safely build a hydrogen fuel cell vehicle, as well as the design challenges faced in the integration of a fuel cell power module, two electric drive motors, a nickel metal hydride battery, and required power electronics into a small sport utility vehicle originally designed for an internal combustion powertrain. The virtual control validation performed involved designing dynamic models of the systems of interest and performing real-time simulation to ensure that the appropriate controller response is observed. For this thesis, emphasis was placed on several key vehicle control topics. Communication robustness was evaluated to ensure that the complicated vehicle communication network could effectively handle traffic from the six powertrain sub-controllers. Safety algorithms were tested for appropriate response to fault conditions. Control systems were developed and tuned offline reducing the amount of time required for in-vehicle development and testing. Software-in-the-loop simulation was used to check initial code integrity and to validate the hardware-in-the-loop vehicle models. The methodology presented in this work was found to be sufficient for a thorough safety and rationality evaluation of control strategies for hybrid fuel cell vehicles.

Vehicle design and control is an attractive area of research in that it embodies a convergence of societal need, technical limitation, and emerging capability. Environmental, political, and monetary concerns are driving the automotive industry towards sustainable transportation, manifested as increasing powertrain electrification in a gradual transition to fossil-free energy vectors. From an electrochemical degradation and control systems perspective, this transition introduces significant technical uncertainty. Initial indications are that the initial battery designs will have twice the required capacity due to degradation concerns. As the battery is a major contributor to the cost of these vehicles the over-sizing represents a significant threat to the ability of OEMs to produce cost-competitive vehicles. This potential barrier is further amplified when the combustion engine is removed and battery-electric or fuel-cell hybrid vehicles are considered. This thesis researches the application of model-based design for optimal design of fuel cell hybrid powertrains considering power source degradation. The intent is to develop and evaluate tools that can determine the optimal sizing and control of the powertrain; reducing the amount of over-sizing by numerically optimization rather than a sub-optimal heuristic design. A baseline hybrid fuel cell vehicle model is developed and validated to a hybrid fuel cell SUV designed and built at the University of Waterloo. Lithium-ion battery degradation models are developed and validated to data captured off a hybrid powertrain test stand built as part of this research. A fuel cell degradation model is developed and integrated into the vehicle model. Lifetime performance is modeled for four hybrid control strategies, demonstrating a significant impact of the hybrid control strategy on powertrain degradation. A plug-in variation of the architecture is developed. The capacity degradation of the battery is found to be more significant than the power degradation. Blended and All-electric charge-depleting hybrid control strategies are integrated and lifetime performance is simulated. The blended charge-depleting control strategy demonstrated significantly less degradation than the all-electric strategy. An oversized battery is integrated into the vehicle model and the benefit of oversizing on reducing the battery degradation rate is demonstrated.

abstract: For years the automotive industry has been shifting towards hybridization and electrification of conventional powertrains due to increase in fossil fuel cost and environmental impact due heavy emission of Green House Gases (GHG) and various pollutants into atmosphere by combustion engine powered vehicles. Hybrid Electric Vehicles (HEV) have proved to achieve superior fuel economy and reduced emissions. Supervisory control strategies determining the power split among various onboard power sources are evolving with time, providing better fuel economies. With increasing complexity of control systems driving HEV’s, mathematical modeling and simulation tools have become extremely advanced and have derived whole industry into adopting Model Based Design (MBD) and Hardware-in-the-loop (HIL) techniques to validate the performance of HEV systems in real world. This report will present a systematic mythology where MBD techniques are used to develop hybrid powertrain, supervisory control strategies and control systems. To validate the effectiveness of various energy management strategies for HEV energy management in a real world scenario, Conventional rule-based power split strategies are compared against advanced Equivalent Consumption Minimization Strategy (ECMS), in software and HIL environment. Since effective utilization of the fuel reduction potential of a HEV powertrain requires a careful design of the energy management control methodology, an advanced ECMS strategy involving implementation with Fuzzy Logic to reduce computational overload has been proposed. Conventional real-time implementation of ECMS based strategy is difficult due to the involvement of heavy computation. Methods like Fuzzy Logic based estimation can be used to reduce this computational overload. Real-time energy management is obtained by adding a Fuzzy Logic based on-the-fly algorithm for the estimation of driving profile and adaptive equivalent consumption minimization strategy (A-ECMS) framework. The control strategy is implemented to function without any prior knowledge of the future driving conditions. The idea is to periodically refresh the energy management strategy according to the estimated driving pattern, so that the Battery State of Charge (SOC) is maintained within the boundaries and the equivalent fuel consumption is minimized. The performance of the presented Fuzzy Logic based adaptive control strategy utilizing driving pattern recognition is benchmarked using a Dynamic Programming based global optimization approach. / Dissertation/Thesis / Masters Thesis Engineering 2015

Rapid advances of computer modeling and simulation tools and computing hardware have turned Model Based Design (MBD) a more viable technology. However, using a computationally intensive, “black-box” form MBD software tool to carry out design optimization leads to a number of key challenges. The non-unimodal objective function and/or non-convex feasible search region of the implicit numerical simulations in the optimization problems are beyond the capability of conventional optimization algorithms. In addition, the computationally intensive simulations used to evaluate the objective and/or constraint functions during the MBD process also make conventional stochastic global optimization algorithms unusable due to their requirement of a huge number of objective and constraint function evaluations. Surrogate model, or metamodeling-based global optimization techniques have been introduced to address these issues. Various surrogate models, including kriging, radial basis functions (RBF), multivariate adaptive regression splines (MARS), and polynomial regression (PR), are built using limited samplings on the original objective/constraint functions to reduce needed computation in the search of global optimum. In many real-world design optimization applications, computationally expensive numerical simulation models are used as objective and/or constraint functions. To solve these problems, enormous fitness function evaluations are required during the evolution based search process when advanced Global Optimization algorithms, such as DIRECT search, Differential Evolution (DE), and Bat Algorithm (BA) are used. In this work, improvements have been made to three widely used global optimization algorithms, Divided Rectangles (DIRECT), Differential Evolution (DE), and Bat Algorithm (BA) by integrating appropriate surrogate modeling methods to increase the computation efficiency of these algorithms to support MBD. The superior performance of these new algorithms in comparison with their original counterparts are shown using commonly used optimization algorithm testing benchmark problems. Integration of the surrogate modeling methods have considerably improved the search efficiency of the DIRECT, DE, and BA algorithms with significant reduction on the Number of Function Evaluations (NFEs). The newly introduced algorithms are then applied to a complex engineering design optimization problem, the design optimization of floating wind turbine platform, to test its effectiveness in real-world applications. These newly improved algorithms were able to identify better design solutions using considerably lower NFEs on the computationally expensive performance simulation model of the design. The methods of integrating surrogate modeling to improve DIRECT, DE and BA global optimization searches and the resulting algorithms proved to be effective for solving complex and computationally intensive global optimization problems, and formed a foundation for future research in this area. / Graduate

Transportation is a primary pollution source contributing to 14 percent of global greenhouse gas emissions, and 12 percent of transportation emissions came from maritime activities. Emissions from the ferry industry, which carries roughly 2.1 billion passengers and 250 million vehicles annually, is a major concern for the general public due to their near-shore operations. Compared to the rapidly advancing clean automotive propulsion, fuel efficiency and emissions improvements for marine vessels are more urgent and beneficial due to the significantly higher petroleum fuel consumption and heavy pollutants and the relatively slow adoption of clean propulsion technology by the marine industry. Hybrid electric propulsion, proven to be effective for ground vehicles, presents a promising solution for more efficient clean marine transportation. Due to the diversified hull/propulsor design and operation cycle, the development of a hybrid electric marine propulsion system demands model-based design and control optimization for each unique and small batch production vessel. The integrated design and control optimization further require accurate and computation efficient hull resistance and propulsor thrust calculation methods that can be used to predict needed propulsion power and gauge vessel performance, energy efficiency, and emissions. This research focuses on improving the low-order empirical hull resistance and propulsor thrust models in the longitudinal direction by extracting model parameters from one-pass computational fluid dynamics (CFD) simulation and testing the acquired models in integrated design optimization of the marine propulsion system. The model is implemented in MATLAB/Simulink and ANSYS Aqwa and validated using operation data from BC Ferries’ ship Tachek. The modified low-order model (M-LOM) is then used in the integrated optimizations of propulsion system component sizes and operation control strategies for another BC Ferries’ ship, Skeena Queen. The performance, energy efficiency, and emissions of various propulsion options, including nature gas-mechanical and natural gas-electric benchmarks, and hybrid electric alternatives of series hybrid, parallel hybrid, and battery/pure electric are compared to demonstrate the benefits of the new method in completing these complex tasks and hybrid electric marine propulsion. The research forms the foundation for further studies to achieve more accurate propulsion demand prediction and a more comprehensive lifecycle cost assessment of clean marine propulsion solutions. / Graduate

The automotive industry is experiencing a rapid increase in software complexity due to various functionalities introduced into modern vehicles. Companies use software development standards like AUTOSAR to develop the application layer software independent from the hardware. Development methodologies such as Model-Based Design are used to increase the efficiency of the development process and decrease the time to market. However, to ensure high-quality software standards such as A-SPICE are imposed on the companies. Conforming to A-SPICE requires having certain traceability between work artifacts. Thus, manual and inefficient development, testing, and requirement management processes lead to higher time to market. This thesis will introduce the concept of virtual validation using VEOS virtual platform from dSPACE. The new toolchain will focus on automatizing the testing process, requirement management, and report generation. To highlight the benefits of the virtual validation concept this new approach will be compared to the existing one, which includes a lot of manual development steps. Besides the application of the virtual validation with AUTOSAR and non-AUTOSAR software architectures will be discussed.