Kernel Based Model Parametrization and Adaptation with Applications to Battery Management Systems.

With the wide spread use of energy storage systems, battery state of health (SOH) monitoring has become one of the most crucial challenges in power and energy research, as SOH significantly affects the performance and life cycle of batteries as well as the systems they are interacting with. Identifying the SOH and adapting of the battery energy/power management system accordingly are thus two important challenges for applications such as electric vehicles, smart buildings and hybrid power systems. This dissertation focuses on the identification of lithium ion battery capacity fading, and proposes an on-board implementable model parametrization and adaptation framework for SOH monitoring. Both parametric and non-parametric approaches that are based on kernel functions are explored for the modeling of battery charging data and aging signature extraction. A unified parametric open circuit voltage model is first developed to improve the accuracy of battery state estimation. Several analytical and numerical methods are then investigated for the non-parametric modeling of battery data, among which the support vector regression (SVR) algorithm is shown to be the most robust and consistent approach with respect to data sizes and ranges. For data collected on LiFePO4 cells, it is shown that the model developed with the SVR approach is able to predict the battery capacity fading with less than 2% error. Moreover, motivated by the initial success of applying kernel based modeling methods for battery SOH monitoring, this dissertation further exploits the parametric SVR representation for real-time battery characterization supported by test data. Through the study of the invariant properties of the support vectors, a kernel based model parametrization and adaptation framework is developed. The high dimensional optimization problem in the learning algorithm could be reformulated as a parameter estimation problem, that can be solved by standard estimation algorithms such as the least-squares method, using a SVR special parametrization. The resulting framework uses the advantages of both parametric and non-parametric methods to model nonlinear dynamics, and greatly reduces the required effort in model development and on-board computation. The robustness and effectiveness of the developed methods are validated using both single cell and multi-cell module data.PhDNaval Architecture and Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116688/1/chsweng_1.pd

Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context

This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0

Machine Learning for Identification and Optimal Control of Advanced Automotive Engines.

The complexity of automotive engines continues to increase to meet increasing performance requirements such as high fuel economy and low emissions. The increased sensing capabilities associated with such systems generate a large volume of informative data. With advancements in computing technologies, predictive models of complex dynamic systems useful for diagnostics and controls can be developed using data based learning. Such models have a short development time and can serve as alternatives to traditional physics based modeling. In this thesis, the modeling and control problem of an advanced automotive engine, the homogeneous charge compression ignition (HCCI) engine, is addressed using data based learning techniques. Several frameworks including design of experiments for data generation, identification of HCCI combustion variables, modeling the HCCI operating envelope and model predictive control have been developed and analyzed. In addition, stable online learning algorithms for a general class of nonlinear systems have been developed using extreme learning machine (ELM) model structure.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102392/1/vijai_1.pd

Design and Optimization of In-Cycle Closed-Loop Combustion Control with Multiple Injections

With the increasing demand of transportation, biofuels play a fundamental role in the transition to sustainable powertrains. For the increased uncertainty of biofuel combustion properties, advanced combustion control systems have the potential to operate the engine with high flexibility while maintaining a high efficiency and robustness. For that purpose, this thesis investigates the analysis, design, implementation, and application of closed-loop Diesel combustion control algorithms. By fast in-cylinder pressure measurements, the combustion evolution can be monitored to adjust a multi-pulse fuel injection within the same cycle. This is referred to as in-cycle closed-loop combustion control.The design of the controller is based on the experimental characterization of the combustion dynamics by the heat release analysis, improved by the proposed cylinder volume deviation model. The pilot combustion, its robustness and dynamics, and its effects on the main injection were analyzed. The pilot burnt mass significantly affects the main combustion timing and heat release shape, which determines the engine efficiency and emissions. By the feedback of a pilot mass virtual sensor, these variations can be compensated by the closed-loop feedback control of the main injection. Predictive models are introduced to overcome the limitations imposed by the intrinsic delay between the control action (fuel injection) and output measurements (pressure increase). High prediction accuracy is possible by the on-line model adaptation, where a reduced multi-cylinder method is proposed to reduce their complexity. The predictive control strategy permits to reduce the stochastic cyclic variations of the controlled combustion metrics. In-cycle controllability of the combustion requires simultaneous observability of the pilot combustion and control authority of the main injection. The imposition of this restriction may decrease the indicated efficiency and increase the operational constraints violation compared to open-loop operation. This is especially significant for pilot misfire. For in-cycle detection of pilot misfire, stochastic and deterministic methods were investigated. The on-line pilot misfire diagnosis was feedback for its compensation by a second pilot injection. High flexibility on the combustion control strategy was achieved by a modular design of the controller. A finite-state machine was investigated for the synchronization of the feedback signals (measurements and model-based predictions), active controller and output action. The experimental results showed an increased tracking error performance and shorter transients, regardless of operating conditions and fuel used.To increase the indicated efficiency, direct and indirect optimization methods for the combustion control were investigated. An in-cycle controller to reach the maximum indicated efficiency increased it by +0.42%unit. The indirect method took advantage of the reduced cyclic variations to optimize the indicated efficiency under constraints on hardware and emission limits. By including the probability and in-cycle compensation of pilot misfire, the optimization of the set-point reference of CA50 increased the indicated efficiency by +0.6unit at mid loads, compared to open-loop operation.Tools to evaluate the total cost of the system were provided by the quantification of the hardware requirements for each of the controller modules

Low Complexity Model Predictive Control of a Diesel Engine Airpath.

The diesel air path (DAP) system has been traditionally challenging to control due to its highly coupled nonlinear behavior and the need for constraints to be considered for driveability and emissions. An advanced control technology, model predictive control (MPC), has been viewed as a way to handle these challenges, however, current MPC strategies for the DAP are still limited due to the very limited computational resources in engine control units (ECU). A low complexity MPC controller for the DAP system is developed in this dissertation where, by "low complexity," it is meant that the MPC controller achieves tracking and constraint enforcement objectives and can be executed on a modern ECU within 200 microseconds, a computation budget set by Toyota Motor Corporation. First, an explicit MPC design is developed for the DAP. Compared to previous explicit MPC examples for the DAP, a significant reduction in computational complexity is achieved. This complexity reduction is accomplished through, first, a novel strategy of intermittent constraint enforcement. Then, through a novel strategy of gain scheduling explicit MPC, the memory usage of the controller is further reduced and closed-loop tracking performance is improved. Finally, a robust version of the MPC design is developed which is able to enforce constraints in the presence of disturbances without a significant increase in computational complexity compared to non-robust MPC. The ability of the controller to track set-points and enforce constraints is demonstrated in both simulations and experiments. A number of theoretical results pertaining to the gain scheduling strategy is also developed. Second, a nonlinear MPC (NMPC) strategy for the DAP is developed. Through various innovations, a NMPC controller for the DAP is constructed that is not necessarily any more computationally complex than linear explicit MPC and is characterized by a very streamlined process for implementation and calibration. A significant reduction in computational complexity is achieved through the novel combination of Kantorovich's method and constrained NMPC. Zero-offset steady state tracking is achieved through a novel NMPC problem formulation, rate-based NMPC. A comparison of various NMPC strategies and developments is presented illustrating how a low complexity NMPC strategy can be achieved.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120832/1/huxuli_1.pd

Data and hybrid models of dynamical systems

Tato práce prezentuje hybridní přístup k modelování dynamiky pomocí kombinace modelování prvních principů a modelování založeného na datech. Využita je unikátní vlastnost RGP, a to že je schopen přizpůsobit svojí dynamiku online, bez nutnosti předsběru dat během trénování, na časově proměnné aerodynamické síly. Navrhujeme metodu RGPMPC, která používá hybridní model v MPC regulátoru, přičemž mění datově založenou složku hybridního modelu tak, aby zohledňovala rozdíly mezi mod- elem a reálným systémem. Metoda je demonstrována na modelu quadrotoru v simulaci, pomocí simulátoru Gazebo. RGPMPC je schopen sledovat požadovanou trajektorii a přizpůsobit se měnícím se aerodynamickým silám. Tento simulační experiment posky- tuje důkaz, že metoda RGPMPC je schopna zlepšit výkon MPC regulátoru v přítom- nosti neznámých rozdílů mezi modelem a reálným systémem.ObhájenoThis thesis presents a hybrid approach for modeling and of dynamics by combining first principles modeling and data-driven modeling. An unique property of the RGP is exploited, namely that it is able to fit the dynamics online, without the need for a training run, to fit time-varying aerodynamics. We propose a method RGPMPC, which uses the hybrid model in a MPC controller, while changing the data-driven component of the hybrid model to account for model discrepancies. We demonstrate our method on a model of a quadrotor in simulation, using the Gazebo simulator. The RGPMPC is able to track the desired trajectory and adapt to the changing drag forces present. This simulation experiment provides a proof of concept that the RGPMPC method is able to improve the performance of the MPC controller in the presence of unknown discrepancies in the model

Optimal control of a motor-integrated hybrid powertrain for a two-wheeled vehicle suitable for personal transportation

The present research aims to propose an optimized configuration of the motor integrated power-train with an optimal controller suitable for small power-train based two wheeler automobile which can increase the system level efficiency without affecting drivability. This work will be the foundation for realizing the system in a production ready vehicle for the two wheeler OEM TVS Motor Company in India. A detailed power-train model is developed (from first principles) for the scooter vehicle, which is powered by a 110 cc spark ignition (SI) engine and coupled with two types of transmission, a continuous variable transmission (CVT) and a 4-speed manual transmission (MT). Both models are capable of simulating torque and NOx emission output of the SI engine and dynamic response of the full power-train. The torque production and emission outputs of the model are compared with experimental results available from TVS Motor Company. The CVT gear ratio model is developed using an indirect method and an analytical model. Both types of powertrain models are applied to perform a simulated study of fuel consumption, NOx emission and drivability study for a particular vehicle platform. In the next stage of work, the mathematical model for a brush-less direct current machine (BLDC) with the drive system and Li-Ion battery are developed. The models are verified and calibrated with the experimental results from TVS Motor Company. The BLDC machine is integrated with both the CVT and MT powertrain models in parallel hybrid configurations and a drive cycle simulation is conducted for different static assist levels by the electrical machines. The initial test confirms the need of optimal sizing of the powertrain components as well as an optimal control system. The detailed model of the powertrain is converted to a control-oriented model which is suitable for optimal control. This is followed by multi-objective optimization of different components of the motor-integrated powertrain using a single function as well as Pareto-Optimal methods. The objective function for the multi-objective optimization is proposed to reduce the fuel consumption with battery charge sustainability with least impact on the increase of financial cost and weight of the vehicle. The optimization is conducted by a nested methodology that involves Particle Swarm Optimization and a Non-dominated sorting genetic algorithm where, concurrently, a global optimal control is developed corresponding to the multi-objective design. The global optimal controller is designed using dynamic programming. The research is concluded with an optimal controller developed using the hp-collocation method. The objective function of the dynamic programming method and hp-collocation method is proposed to reduce fuel consumption with battery charge sustainability.Open Acces

Selected aspects of providing the chemmotological reliability of the engineering

Transport sector is an important component of the economy that have an impact on the development and prosperity of the population. Rational use of fuels and lubricants, energy efficiency, environmental safety are included into the list of the most important problems of the modern world. Solving these problems determines in a great manner the sustainable development of the world economy and keeping comfort conditions for human being. Efficiency, reliability of operation of vehicles, rational use of operational materials depend on their correct selection. According to its quality operational materials must conform to both the model and operating conditions of vehicles. The use of poor quality materials leads to a decrease in the durability and reliability of machinery and machine parts; the use of materials of higher quality than required causes unreasonable increase in costs. The knowledge of machinery suggest not only the knowledge of construction, kinematic, dynamic, and temperature characteristics but also physico-chemical properties of constituent materials that are necessary for analyzing and forecasting of physico-chemical processes during use of a Fuels or a Lubricants. Thus, the efficiency and reliability of vehicles operation depends not only on their structural characteristics, but also on the optimal selection of Fuels and Lubricants, Technical Liquids and other Operational Materials. Work professional activity of specialists dedicated to petroleum refinery, organizing of storage, transportation and distribution of products, assurance of correspondence between the properties of Fuels, Lubricants, Technical liquids and the conditions of operation of technology and engines aimed at obtaining maximum technical, economical, ecological and social effects is called usage of Fuels, Lubricants and Technical liquids. To know Fuels, Lubricants and Technical liquids is to clearly understand the interconnection of quality parameters with physico-chemical and energy processes, occurring in the process of their use under specific conditions, and also the connection with their chemical and group composition. The knowledge of technology suggest not only the knowledge of construction, kinematic, dynamic, and temperature characteristics but also physico-chemical properties of constituent materials that are necessary for analyzing and forecasting of physico-chemical processes during use of a Fuel or a Lubricant. The study of the essence, regularity (tendens) and connections of phenomena and the processes of use of Fuels, Lubricants, Technical liquids in Aviation Technology with the help of special methodological tools is the base of Aviation Chemmotology. Aviation Chemmotology is a part of Chemmotology that studies and solves the problems of ensuring the necessary quality and application requirements of Fuels and Lubricants used in Aviation Technology. Chemmotological reliability is a reliability of technology depending on the Quality of Fuels and Lubricants (the ability of technology to maintain good reliability when operated with Fuels and Lubricants grades that are of a economically reasonable quality level). This monograph as an intergative scientific work of many scholars is a striking example of the representation of these aspects and really illustrates the modern consolidated work of scientists and practitioners, trends in the development of scientific schools of different universities, different countries and science in general. Because, as is know, science does not have borders. Scientific achievements are global civilizational heritage

Machine Learning and Its Application to Reacting Flows

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation