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This book mainly investigates the cooperative optimal control of hybrid energy system, it presents security control, multi-objective optimization, distributed optimization and distributed control approaches for tackling with security, economic and stability problem of the hybrid energy system. It aims to solve some challenging problems including security issue, economic cost or benefits from both power generation side and load demand side, and coordination among different power generators. The methods proposed in this book is novel and attractive, it consists of the hierarchical optimal control strategy for the security issue, multi-objective optimization for both economic and emission issue, and distributed optimal control for coordination among power generators. Readers can learn novel methods or technique for tackling with the security issue, multiple-objective problem, and distributed coordination problem. It also may inspire readers to improve some drawbacks of existing alternatives. Some fundamental knowledge prepared to read this book includes basic principles of the multi-agents system, robust optimization, Pareto-dominance optimization, and background of electrical engineering and renewable energy.
This book assembles new methods showing the automotive engineer for the first time how hybrid vehicle configurations can be modeled as systems with discrete and continuous controls. These hybrid systems describe naturally and compactly the networks of embedded systems which use elements such as integrators, hysteresis, state-machines and logical rules to describe the evolution of continuous and discrete dynamics and arise inevitably when modeling hybrid electric vehicles. They can throw light on systems which may otherwise be too complex or recondite. Hybrid Systems, Optimal Control and Hybrid Vehicles shows the reader how to formulate and solve control problems which satisfy multiple objectives which may be arbitrary and complex with contradictory influences on fuel consumption, emissions and drivability. The text introduces industrial engineers, postgraduates and researchers to the theory of hybrid optimal control problems. A series of novel algorithmic developments provides tools for solving engineering problems of growing complexity in the field of hybrid vehicles. Important topics of real relevance rarely found in text books and research publications—switching costs, sensitivity of discrete decisions and there impact on fuel savings, etc.—are discussed and supported with practical applications. These demonstrate the contribution of optimal hybrid control in predictive energy management, advanced powertrain calibration, and the optimization of vehicle configuration with respect to fuel economy, lowest emissions and smoothest drivability. Numerical issues such as computing resources, simplifications and stability are treated to enable readers to assess such complex systems. To help industrial engineers and managers with project decision-making, solutions for many important problems in hybrid vehicle control are provided in terms of requirements, benefits and risks.
Optimal Control of Hybrid Vehicles provides a description of power train control for hybrid vehicles. The background, environmental motivation and control challenges associated with hybrid vehicles are introduced. The text includes mathematical models for all relevant components in the hybrid power train. The power split problem in hybrid power trains is formally described and several numerical solutions detailed, including dynamic programming and a novel solution for state-constrained optimal control problems based on the maximum principle. Real-time-implementable strategies that can approximate the optimal solution closely are dealt with in depth. Several approaches are discussed and compared, including a state-of-the-art strategy which is adaptive for vehicle conditions like velocity and mass. Three case studies are included in the book: • a control strategy for a micro-hybrid power train; • experimental results obtained with a real-time strategy implemented in a hybrid electric truck; and • an analysis of the optimal component sizes for a hybrid power train. Optimal Control of Hybrid Vehicles will appeal to academic researchers and graduate students interested in hybrid vehicle control or in the applications of optimal control. Practitioners working in the design of control systems for the automotive industry will also find the ideas propounded in this book of interest.
In this two-part study, time-series models are used to determine optimal dispatch strategies, in conjunction with optimally-sized components, in remote hybrid power systems. The objective of the dispatch optimization is to minimize the costs associated with diesel fuel, diesel starts, and battery erosion, based on a thorough economic analysis of present worth life-cycle cost. An ideal predictivecontrol strategy is used as a basis of comparison. In Part I (reported here), a simplified time-series model is used to obtain preliminary conceptual results. These results illustrate the nature of the optimal dispatch strategy and indicate that a simple SOC setpoint strategy can be practically as effective as the ideal predictive control. In Part II (at a later date), a more detailed model willbe used to obtain more accurate, quantitative results. We anticipate that these results will be correlated to dimensionless economic, design, and performance parameters, rendering them useful as design guidelines over a wide variety of load profiles, climates, equipment specifications, and economic variables.
The three-volume set CCIS 1467, CCIS 1468, and CCIS 1469 constitutes the thoroughly refereed proceedings of the 7th International Conference on Life System Modeling and Simulation, LSMS 2021, and of the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, held in Hangzhou, China, in October 2021. The 159 revised papers presented were carefully reviewed and selected from over 430 submissions. The papers of this volume are organized in topical sections on: Medical Imaging and Analysis Using Intelligence Computing; Biomedical signal processing, imaging, visualization and surgical robotics; Computational method in taxonomy study and neural dynamics; Intelligent medical apparatus, clinical applications and intelligent design of biochips; Power and Energy Systems; Computational Intelligence in Utilization of Clean and Renewable Energy Resources, and Intelligent Modelling, Control and Supervision for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Control Methods in Energy Infrastructure Development and Distributed Power Generation Systems; Intelligent Modeling, Simulation and Control of Power Electronics and Power Networks; Intelligent Techniques for Sustainable Energy and Green Built Environment, Water Treatment and Waste Management; Intelligent Robot and Simulation; Intelligent Data Processing, Analysis and Control in Complex Systems; Advanced Neural Network Theory and Algorithms; Advanced Computational Methods and Applications; Fuzzy, Neural, and Fuzzy-neuro Hybrids; Intelligent Modelling, Monitoring, and Control of Complex Nonlinear Systems; Intelligent manufacturing, autonomous systems, intelligent robotic systems; Computational Intelligence and Applications.
This book investigates distributed cooperative control and communication of MASs including linear systems, nonlinear systems and multiple rigid body systems. The model-based and data-driven control method are employed to design the (optimal) cooperative control protocol. The approaches of this book consist of model-based and data-driven control such as predictive control, event-triggered control, optimal control, adaptive dynamic programming, etc. From this book, readers can learn about distributed cooperative control methods, data-driven control, finite-time stability analysis, cooperative attitude control of multiple rigid bodies. Some fundamental knowledge prepared to read this book is finite-time stability theory, event-triggered sampling mechanism, adaptive dynamic programming and optimal control.
Hybrid Energy System Models presents a number of techniques to model a large variety of hybrid energy systems in all aspects of sizing, design, operation, economic dispatch, optimization and control. The book's authors present a number of new methods to model hybrid energy systems and several renewable energy systems, including photovoltaic, solar plus wind and hydropower, energy storage, and combined heat and power systems. With critical modeling examples, global case studies and techno-economic modeling integrated in every chapter, this book is essential to understanding the development of affordable energy systems globally, particularly from renewable resources. With a detailed overview and a comparison of hybrid energy systems used in different regions, as well as innovative hybrid energy system designs covered, this book is useful for practicing power and energy engineers needing answers for what factors to consider when modeling a hybrid energy system and what tools are available to model hybrid systems. Combines research on several renewable energy systems, energy storage, and combined heat and power systems into a single informative resource on hybrid energy systems Includes significant global case studies of current and novel modeling techniques for comparison Covers numerical simulations of hybrid systems energy modeling and applications