Date of Award

12-2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Automotive Engineering

Committee Member

Dr. Simona Onori, Committee Chair

Committee Member

Dr. Ilenia Battiato

Committee Member

Dr. Mark Hoffman

Committee Member

Dr. Robert Prucka

Committee Member

Dr. Fadi Abu-Farha

Abstract

Technological advancements and globalization in recent decades have largely been responsible for the ever-increasing energy and power demands across different industrial sectors. This has led to an extensive use of fossil fuel based resources such as gasoline and diesel, especially in the transportation industry [1]. The consequences of this utilization are excessive emission of greenhouse gases and degradation of air quality, which have raised significant environmental concerns. Added to this, concerns over the eventual depletion of fossil fuels has accelerated the exploration and development of new energy sources. At the same time, increasingly stringent regulations have been imposed to enhance the fuel efficiency and minimize emissions in automobiles. Efforts to meet current and future regulation targets have led to the development of new technologies, some of which are: a) vehicle electrification [2], b) gasoline direct injection technology [3], c) variable valve timing [4], d) advanced exhaust gas recirculation [5], and e) selective catalytic reduction for NOx [6]. On the energy front, wind and solar technologies have been vastly explored [7], but these technologies are time-dependent and intermittent in nature and must be supplemented by energy storage devices. Lithium-ion batteries have been considered the most preferred technology for grid energy storage and electrified transportation because of their higher energy and power densities, better efficiency, and longer lifespan in comparison with other energy storage devices such as lead acid, nickel metal hydride, and nickel cadmium [8]. Lithium-ion batteries are the most dominant technology today in small scale applications such as portable phones and computers [9]. However, their wide-scale adoption in automotive and grid energy storage applications has been hampered by concerns associated with battery life, safety, and reliability. A lack of comprehensive understanding of battery behavior across different environments and operating conditions make it challenging to extract their best performance. Currently, significant trade-offs are being made to optimize battery performance, such as over-sizing and under-utilization in automotive applications. While sensors are used to evaluate battery performance and regulate their operation, their fundamental limitation lies in the inability to measure battery internal states such as state-of-charge (SoC) or state-of-health (SoH). The aforementioned issues with lithium-ion batteries can addressed to a large extent with the help of mathematical modeling. They play an important role in the design and utilization of batteries in an efficient manner with existing technologies, because of their ability to predict battery behavior with minimal expenditure of time and materials [10]. While empirical mathematical models are computationally efficient, they rely on a significant amount of experimental data and calibration effort to predict future battery behavior. In addition, such models do not consider the underlying physicochemical transport processes and hence cannot predict battery degradation. Moreover, the knowledge acquired from such models cannot be generalized across different battery chemistry and geometry. This elucidates the need for fundamental physics-based mathematical models to aid in the development of advanced control strategies through model-based control and virtual sensor deployment. Such models can capture the underlying transport phenomena across various length and time scales, and enhance performance and longevity of batteries while ensuring safe operation. The overarching aim of this dissertation is to present a multiscale modeling approach that captures the behavior of such devices with high fidelity, starting from fundamental principles. The application of this modeling approach is focused on porous lithium-ion batteries. The major outcome of this work is to facilitate the development of advanced and comprehensive battery management systems by: a) developing a high fidelity multiscale electrochemical modeling framework for lithium-ion batteries, b) investigating the temperature-influenced and aging-influenced multiscale dynamics for different battery chemistry and operating conditions, c) formulating a methodology to analytically determine effective ionic transport properties using the electrode microstructure, and d) numerical simulation of the developed physics-based model and comparison analysis with the conventionally used Doyle-Fuller-Newman (DFN) electrochemical model. The new multiscale model presented in this dissertation has been derived using a rigorous homogenization approach which uses asymptotic expansions of variables to determine the macroscopic formulation of pore-scale governing transport equations. The conditions that allow successful upscaling from pore-to-macro scales are schematically represented using 2-D electrode and electrolyte phase diagrams. These phase diagrams are used to assess the predictability of macroscale models for different electrode chemistry and battery operating conditions. The effective transport coefficients of the homogenized model are determined by resolving a unit cell closure variable problem in the electrode microstructure, instead of conventionally employed empirical formulations. The equations of the developed full order homogenized multiscale (FHM) model are implemented and resolved using the finite element software COMSOL Multiphysics®. Numerical simulations are presented to demonstrate the enhanced predictability of the FHM against the traditionally used DFN model, particularly at higher temperatures of battery operation. Model parameter identification is performed by co-simulation studies involving COMSOL Multiphysics® and MATLAB® software using the Particle Swarm Optimization (PSO) technique. The parameter identification studies are performed using data from laboratory experiments conducted on 18650 cylindrical lithium-ion cells of nickel-manganese-cobalt oxide (NMC) cathode chemistry.

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