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Abstract : Global warming has led to increased research in renewable energy and the need for efficient energy storage systems. Lithium-ion batteries are a promising solution, but their performance degrades at high temperatures. To improve thermal management, researchers are exploring the use of phase change materials (PCMs) combined with fin structures. Different fin geometries impact heat dissipation. The goal of this study is to perform a reliability-based design optimization of a battery thermal management system for a desired reliability and temperature level. The design geometry consists of four components that include the lithium-ion cell at the core having a fin structure with a PCM module attached to it, and an acrylic shell on the outside. The geometric design variables include the dimension of the outer radius of the battery shell (overall diameter of the battery) and three dimensions of a T-shaped fin structure. Along with the four design variables, two uncertainty parameters of battery heat generation that happens at the core and the ambient convective heat transfer coefficient on the outer surface are considered for the reliability based design optimization. Latin Hypercube Sampling is used to generate sample points for thermal analysis that is done using ANSYS Mechanical APDL. These data points are used to train a machine learning model to predict temperatures for unknown design samples during the optimization process. The optimization is done using a type of an evolutionary algorithm. Initially the optimization problem was formulated using a single objective function that was minimized to find the optimal design configuration. The results of this optimization encouraged to pursue the ix possibility of multiple optimal solutions and formulate a multi-objective optimization problem.
This book introduces the fundamentals of probability, statistical, and reliability concepts, the classical methods of uncertainty quantification and analytical reliability analysis, and the state-of-the-art approaches of design optimization under uncertainty (e.g., reliability-based design optimization and robust design optimization). The topics include basic concepts of probability and distributions, uncertainty quantification using probabilistic methods, classical reliability analysis methods, time-variant reliability analysis methods, fundamentals of deterministic design optimization, reliability-based design optimization, robust design optimization, other methods of design optimization under uncertainty, and engineering applications of design optimization under uncertainty.
Thermal Management of Batteries presents a comprehensive examination of the various conventional and emerging technologies used for thermal management of batteries and electronics. With an emphasis on advanced nanofluids, the book provides step-by-step guidance on advanced techniques at the component and system level for both active and passive technologyStarting with an overview of the fundamentals, each chapter quickly builds into a comprehensive treatment of up-to-date technologies. The first part of the book discusses advanced battery technologies, while the second part addresses the design and performance optimization of battery thermal management systems. Power density and fast charging mechanisms of batteries are considered, as are role of thermal management systems on performance enhancement. The book discusses the design selection of various thermal management systems, parameters selection for different configurations, the operating conditions for different battery types, the setups used for experimentation and instrumentation, and the operation of thermal management systems. Advanced techniques such as heat pipes, phase change materials, nanofluids, novel heat sinks, and two phase flow loops are examined in detail.Presenting the fundamentals through to the latest developments alongside step-by-step guidance, mathematical models, schematic diagrams, and experimental data, Thermal Management of Batteries is an invaluable and comprehensive reference for graduates, researchers, and practicing engineers working in the field of battery thermal management, and offers valuable solutions to key thermal management problems that will be of interest to anyone working on energy and thermal heat systems. Critically examines the components of batteries systems and their thermal energy generation Analyzes system scale integration of battery components with optimization and better design impact Explores the modeling aspects and applications of nanofluid technology and PCMs, as well as the utilization of machine learning techniques Provides step-by-step guidance on techniques in each chapter that are supported by mathematical models, schematic diagrams, and experimental data
During the past decade, design under uncertainty has received significant attention and a wide range of applications from designing simple product components to designing complex and emerging engineered systems. Uncertainty is ubiquitous in engineering design. Therefore, it is crucial to discover an optimized design that satisfies the reliability requirements. A commonly used design optimization methodology for engineering systems comprises deterministic modeling and simulation-based design optimization. However, traditional deterministic design optimization (DDO) is not capable of considering design uncertainty. Taking uncertainties into account could be handled by using a technique called Reliability-based design optimization (RBDO). Reliability analysis, as the fundamental process in RBDO, evaluates the probability of failure as a probability of a function to reach a defined limit state. RBDO requires reliability analysis to estimate probabilistic design constraints iteratively, and it can be very time consuming because the reliability analysis requires huge numbers of input data. Hence, efficient reliability analysis is inevitable especially when computationally expensive simulation such as finite element runs is involved. The ultimate purpose for any reliability analysis problem is to increase the efficiency and accuracy of the calculations. There are still clear deficiencies in terms of efficiency and accuracy of the solution methods which is used depending on the nature of the problem (level of nonlinearity and size). The aim of this research is to propose efficient methods for reliability analysis in the design of energy harvesters and sensor network systems. We apply these techniques to high dimensional RBDO that possibly involves finite element analyses. Dimension reduction (DR) method is implemented in the first part of this dissertation to address design under uncertainty for complex engineering problems. Next, an adaptive improved response surface method (AIRSM) in conjunction with a new regression based model is proposed for solving reliability problems and compared with DR method in terms of efficiency and accuracy. Finally, a high dimensional complex reliability problem is solved using Kriging model and Genetic Algorithm (GA). This research aims to suggest practical and efficient reliability analysis methods to minimize the computational cost while maintaining accuracy to be integrated into the RBDO.
Thermal Management of Electric Vehicle Battery Systems provides a thorough examination of various conventional and cutting edge electric vehicle (EV) battery thermal management systems (including phase change material) that are currently used in the industry as well as being proposed for future EV batteries. It covers how to select the right thermal management design, configuration and parameters for the users’ battery chemistry, applications and operating conditions, and provides guidance on the setup, instrumentation and operation of their thermal management systems (TMS) in the most efficient and effective manner. This book provides the reader with the necessary information to develop a capable battery TMS that can keep the cells operating within the ideal operating temperature ranges and uniformities, while minimizing the associated energy consumption, cost and environmental impact. The procedures used are explained step-by-step, and generic and widely used parameters are utilized as much as possible to enable the reader to incorporate the conducted analyses to the systems they are working on. Also included are comprehensive thermodynamic modelling and analyses of TMSs as well as databanks of component costs and environmental impacts, which can be useful for providing new ideas on improving vehicle designs. Key features: Discusses traditional and cutting edge technologies as well as research directions Covers thermal management systems and their selection for different vehicles and applications Includes case studies and practical examples from the industry Covers thermodynamic analyses and assessment methods, including those based on energy and exergy, as well as exergoeconomic, exergoenvironmental and enviroeconomic techniques Accompanied by a website hosting codes, models, and economic and environmental databases as well as various related information Thermal Management of Electric Vehicle Battery Systems is a unique book on electric vehicle thermal management systems for researchers and practitioners in industry, and is also a suitable textbook for senior-level undergraduate and graduate courses.
Uncertainties in Modern Power Systems combines several aspects of uncertainty management in power systems at the planning and operation stages within an integrated framework. This book provides the state-of-the-art in electric network planning, including time-scales, reliability, quality, optimal allocation of compensators and distributed generators, mathematical formulation, and search algorithms. The book introduces innovative research outcomes, programs, algorithms, and approaches that consolidate the present status and future opportunities and challenges of power systems. The book also offers a comprehensive description of the overall process in terms of understanding, creating, data gathering, and managing complex electrical engineering applications with uncertainties. This reference is useful for researchers, engineers, and operators in power distribution systems. Includes innovative research outcomes, programs, algorithms, and approaches that consolidate current status and future of modern power systems Discusses how uncertainties will impact on the performance of power systems Offers solutions to significant challenges in power systems planning to achieve the best operational performance of the different electric power sectors
This book focuses on the thermal management technology of lithium-ion batteries for vehicles. It introduces the charging and discharging temperature characteristics of lithium-ion batteries for vehicles, the method for modeling heat generation of lithium-ion batteries, experimental research and simulation on air-cooled and liquid-cooled heat dissipation of lithium-ion batteries, lithium-ion battery heating method based on PTC and wide-line metal film, self-heating using sinusoidal alternating current. This book is mainly for practitioners in the new energy vehicle industry, and it is suitable for reading and reference by researchers and engineering technicians in related fields such as new energy vehicles, thermal management and batteries. It can also be used as a reference book for undergraduates and graduate students in energy and power, electric vehicles, batteries and other related majors.
Recent developments in reliability engineering has become the most challenging and demanding area of research. Modeling and Simulation, along with System Reliability Engineering has become a greater issue because of high-tech industrial processes, using more complex systems today. This book gives the latest research advances in the field of modeling and simulation, based on analysis in engineering sciences. Features Focuses on the latest research in modeling and simulation based analysis in reliability engineering. Covers performance evaluation of complex engineering systems Identifies and fills the gaps of knowledge pertaining to engineering applications Provides insights on an international and transnational scale Modeling and Simulation Based Analysis in Reliability Engineering aims at providing a reference for applications of mathematics in engineering, offering a theoretical sound background with adequate case studies, and will be of interest to researchers, practitioners, and academics.