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The market for Li-ion batteries has seen unprescedented growth in recent years due to the adoption of electric vehicles (EVs) and growth of grid-level energy storage. For these applications to be sustainable, inexpensive and long-lasting Li-ion batteries are required. This thesis considers LiFePO4 (LFP) as a positive electrode material for use in long-lifetime Li-ion batteries. Already a commercially used material, LFP is seeing a renewed interest in many applications due to the cost and relative scarcity of commonly used transition metals in Li-ion batteries, Ni and Co. Initial studies of LFP/graphite cells considered the impact of water contamination and different electrolyte additives on lifetime, and an optimal electrolyte composition was determined. Isothermal microcalorimetry techniques were used to rank the lifetime of cells with different electrolyte additives. Next, different approaches were taken to improve the lifetime of LFP/graphite cells, including considering the surface area of LFP, different Li salts in the electrolyte, and different graphite materials. Combining the results of these studies led to an LFP cell with greatly improved capacity retention. Isothermal microcalorimetry techniques were developed to observe parasitic reactions separately at the positive and negative electrodes, and to infer the degree of "cross-talk" reactions in the cell. Finally, the storage performance, gas evolution, and parasitic heat flow for Li-ion cells with different positive electrodes, negative electrodes, and electrolytes were studied. The results of these experiments highlighted the complex interactions that occur between different components of the cell. In LFP cells, capacity loss was correlated with the reactivity of the negative electrode. The results presented in this thesis demonstrate significant lifetime improvements for LFP/graphite cells by targeting different cell components. Additional insights into the role of parasitic reactions on the lifetime of Li-ion cells have been developed. This work should contribute to the future development of Li-ion cells with extremely long lifetimes.
This book addresses the comprehensive understanding of Ni-rich layered oxide of lithium-ion batteries cathodes materials, especially focusing on the effect of dopant on the intrinsic and extrinsic effect to its host materials. This book can be divided into three parts, that is, 1. overall understanding of layered oxide system, 2. intrinsic effect of dopant on layered oxides, and 3. extrinsic effect of dopant on layered oxides. To truly understand and discover the fundamental solution (e.g. doping) to improve the Ni-rich layered oxides cathodic performance, understanding the foundation of layered oxide degradation mechanism is the key, thus, the first chapter focuses on discovering the true degradation mechanisms of layered oxides systems. Then, the second and third chapter deals with the effect of dopant on alleviating the fundamental degradation mechanism of Ni-rich layered oxides, which we believe is the first insight ever been provided. The content described in this book will provide research insight to develop high-performance Ni-rich layered oxide cathode materials and serve as a guide for those who study energy storage systems. ​
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The integration of lithium ion batteries into the electric vehicle market is dependent on a few key issues. Two of these issues include cycle life and performance at low temperatures. Industrial lithium-ion batteries need to be able to withstand several thousand cycles and operate over a large temperature range so as not to induce range anxiety in drivers in colder climates. One of the major degradation mechanisms that can occur within a battery, especially in cold temperatures, is lithium plating on the graphite anode. Experiments in this dissertation show that the degradation mechanism of LiNi0.6Mn0.2Co0.2O2/graphite high energy cells that causes a premature death is lithium plating.Experiments utilizing specially fabricated research-type cells with forced lithium plating are used to better understand the important variables that drive lithium plating in graphite anodes. Results show that temperature plays a dominant role in the onset of lithium plating, which can even occur at temperatures above 0 oC. In order to mitigate lithium plating in Li-ion batteries, the internal temperature of the battery must be monitored and controlled. This can be obtained through self-heating quickly and efficiently while using a minimal amount of energy. Pulse heating of batteries from 0 oC is examined in order to decrease spatial temperature gradients, lower average activation current and minimize wasted energy due to temperature overshoot when compared to the current continuous heating protocol. The optimal pulse heating protocol is a 2 second on-pulse followed by a 2 second off-pulse. Mitigation of lithium plating in industrial electric vehicle batteries is attempted utilizing the self-heating technology. External heating is unable to heat the battery from sub-zero temperatures while inducing a safety shut-off and large spatial temperature gradients. Conversely, internal heating is successful at heating the cell quickly and uniformly with minimal energy consumed.
"The growing interests in Lithium-ion Batteries (LIBs) have significantly accelerated the development of active materials. However, the key challenge is that electrode materials suffer from degradation, which include transition metal dissolution, solid electrolyte interphase (SEI) layer formation, and mechanical fracture. To address these issues, applying an ultrathin coating onto active materials via Atomic Layer Deposition (ALD) is an efficient way. Although numerious works have been done for active material performance improvement via ALD technology, the fundamental enhancement mechanisms of ALD coating on battery performance improvement are not yet known. Therefore, this dissertation consists of four papers, which focused on the ALD coating impact on Li intercalation, metal dissolution, Li ion diffusivity and interfacial property of SEI layer via first-principles study. Paper I explained why CeO2 coating has better performance than Al2O3 coating material via faster Li diffusion, facile intercalation, and less mechanical damage of coating. Paper II discovered an unexpected metal dissolution that ultrathin CeO2 coating intensifies the Mn dissolution of LMO and it was confirmed in several ways, including ICP-OES measurement, Mn vacancy formation energy calculation, COOP analysis, PDOS analysis, and cell level performance. Paper III revealed that the ALD CeO2 coating thickness impact on Li ion diffusivity in coated LMO is related to surface and bulk diffusion domination and phase transition of coating layers. Paper IV demonstrated that the fracture strength of inorganic components of SEI layer was higher than organic component, implying that the inorganic-organic interface can effectively block electron transport from electrolyte to anode particles to prevent futher oxidation of active materials"--Abstract, page iv.
Lithium-ion batteries have become increasingly prevalent in everyday life, from mobile devices to electric vehicles. In order to swiftly and robustly deploy lithium-ion batteries at large scale in a wide range of applications, an understanding of battery degradation as a function of operating conditions is critical. This dissertation focuses on building this understanding by generating extensive battery cycling datasets and applying a data-driven diagnosis methodology to diagnose the root causes of degradation. In Chapter 1, we introduce lithium-ion batteries and their importance to the global energy landscape. I explain the fundamental internal processes behind lithium ion battery operation and highlight the degradation mechanisms, degradation modes, and performance metrics that we use to describe battery aging. In Chapter 2, we establish a data-driven degradation diagnosis framework that combines degradation inducing aging cycles with diagnostic cycles to probe fundamental degradation modes (lithium inventory, positive electrode capacity, negative electrode capacity, and resistance increase) and device performance metrics over the course of battery lifetime. We apply interpretable machine learning methods to deconvolute the effects of different input parameters on the target outputs (degradation modes and performance metrics). This framework is used to design battery cycling experiments and analyze battery cycling data. In Chapter 3, we apply this framework first to an exploratory dataset to compare the relative importances of key operating conditions on degradation modes and performance metrics. The key results from this study are that charging conditions (charging current and cutoff voltage) have the highest impact on many degradation modes and performance metrics. However, discharging current is the most important factor for a few important degradation modes, and varies widely between devices of the same type depending on the user or application. These results provide the foundation and motivation for our main work: a study on degradation as a function of realistic usage conditions. In Chapter 4, we generate a novel, extensive application-relevant dataset with diverse realistic discharge protocols. We then apply the data-driven degradation diagnosis framework to relate the effects of dynamic operating conditions to lithium-ion battery degradation modes and device performance. We first demonstrate that constant current discharging conditions are not representative of realistic use cases, and that diverse discharge profiles lead to differences in degradation. We find that higher rest states of charge predict higher resistance and shorter cycle life, and that larger values of the higher characteristic frequency predict larger resistances. Finally we reveal that under these realistic discharging conditions, cycling time appears to be more relevant than cycle number for analyzing degradation. In Chapter 5, we summarize the conclusions from all chapters of this work, focusing particularly on the insights from Chapter \ref{chap:realistic}. We also use this chapter to explore future studies that can build upon the results of this work. Proposed work includes both further battery cycling experiments and fundamental studies probing the relationships revealed by the data-driven degradation diagnostics framework. Unrelated to data-driven degradation diagnostics, my first project was investigating the use of eutectic mixtures of quinones as a high energy density redox flow battery electrolyte. In Appendix C, I'll describe some of the work I did supporting this project that are not included in the publications of this study. In Appendix D, I detail the work I did on melting point prediction for small organic redox-active molecules, quinones and hydroquinones. At the beginning of each chapter, I'll establish my specific contributions to the work being described. Additionally, given that data-driven approaches for understanding lithium-ion battery degradation have gained significant traction in recent years, I'll establish the scope of existing works (to the best of my knowledge) near the beginning of each relevant chapter to provide more context for the novelty that this work brings to the field.