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We are witnessing a fast-growing demand in vehicle electrification nowadays due to the widespread environmental consciousness, stringent emission regulations, and carbon neutrality implementation. As one of the most promising energy storage and electrification solutions, lithium-ion battery has been widely employed for electric vehicles (EVs) due to its excellent properties like high energy density, low maintenance, and long cycle life. However, there still exist multiple critical challenges in using lithium-ion battery at large scale as the major power source, such as reliability issues, safety concerns, and especially the range anxiety. Several promising solutions have been explored in the EV industry to mitigate the drawback of range anxiety, such as larger capacity with high energy density and ultra-fast charging. All these approaches challenge the temperature sensitive battery system as a side effect by bringing in extra overburdened waste heat. Given these concerns, battery thermal management system (BTMS) plays an indispensable role in maintaining the maximum temperature and temperature uniformity for EVs. This dissertation proposes a novel J-type air-based cooling structure via re-designing conventional U- and Z- type structures. Aiming to further improve the thermal performance, a surrogate-based optimization framework with two-stage cluster-based resampling is developed for BTMS structural optimization. Compared with the U- and Z- type, the novel J-type structure is proved with significant advancements. Based on the optimized J-type configuration, an operation mode switching module is designed to mitigate the temperature unbalance by controlling the opening degree of two outlet valves. Tested by an integrated driving cycle, results reveal that the J-type structure with its appropriate control strategy is a promising solution for light-duty EVs using an air cooling technology. Improving the energy efficiency is another potential approach to mitigate range anxiety. In this dissertation, a model predictive control (MPC)-based energy management strategy is developed to simultaneously control the BTMS, the air conditioning system, and the regenerative power. A vehicle velocity forecasting framework is integrated with the MPC-based energy management to further improve the energy efficiency. Deep learning and image-based traffic light detection techniques have been leveraged for velocity forecasting. Results show that the proposed energy management method has significantly improved the overall EV energy efficiency.
This Special Edition of Energies on “Energy Storage and Management for Electric Vehicles” draws together a collection of research papers that critically evaluates key areas of innovation and novelty when designing and managing the high-voltage battery system within an electrified powertrain. The addressed topics include design optimisation, mathematical modelling, control engineering, thermal management, and component sizing.
The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components.
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.
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.
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.
The battery management system (BMS) optimizes the efficiency of batteries under allowable conditions and prevents serious failure modes. This book focuses on critical BMS techniques, such as battery modeling; estimation methods for state of charge, state of power and state of health; battery charging strategies; active and passive balancing methods; and thermal management strategies during the entire lifecycle. It also introduces functional safety and security-related design for BMS, and discusses potential future technologies, like digital twin technology.
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
The electrification of commercial medium- and heavy-duty (MD/HD) vehicles presents new challenges in the design and control of the hybrid powertrain. Advances in Li-Ion battery technology have increased the capabilities of the battery pack, providing more electric range and increased power characteristics. However, new approaches and tools are necessary to improve the overall system efficiency, where a single cell technology may not meet the vocation demand. This dissertation explores the use of a convex optimization framework for the optimal design, energy, and thermal management of Li-Ion battery packs. Convex optimization methods provide advantages in terms of problem formulation, computation speed, and the guarantee of a globally optimal solution. Leveraging a convex framework, new hybrid energy storage systems (HESS) are investigated, where high-energy and high-power batteries are combined in a single system to capitalize on the benefits of each technology. The implementation of a HESS introduces complexities in the pack design and energy management, which are presented through a Design Space Exploration comparing single chemistry and hybrid chemistry battery packs. In addition, the optimal control of the battery thermal management system (TMS) is developed using convex optimization, where new methodologies are explored to mitigate the propagation of approximation error that arise during the convexification process.