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Degradation Mechanisms And Lifetime Prediction For Lithium Ion Batteries A Control Perspective Preprint
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Book Synopsis Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint by :
Download or read book Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint written by and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.
Book Synopsis Advances in Lithium-Ion Batteries for Electric Vehicles by : Haifeng Dai
Download or read book Advances in Lithium-Ion Batteries for Electric Vehicles written by Haifeng Dai and published by Elsevier. This book was released on 2024-02-26 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Lithium-Ion Batteries for Electric Vehicles: Degradation Mechanism, Health Estimation, and Lifetime Prediction examines the electrochemical nature of lithium-ion batteries, including battery degradation mechanisms and how to manage the battery state of health (SOH) to meet the demand for sustainable development of electric vehicles. With extensive case studies, methods and applications, the book provides practical, step-by-step guidance on battery tests, degradation mechanisms, and modeling and management strategies. The book begins with an overview of Li-ion battery aging and battery aging tests before discussing battery degradation mechanisms and methods for analysis. Further methods are then presented for battery state of health estimation and battery lifetime prediction, providing a range of case studies and techniques. The book concludes with a thorough examination of lifetime management strategies for electric vehicles, making it an essential resource for students, researchers, and engineers needing a range of approaches to tackle battery degradation in electric vehicles. Evaluates the cause of battery degradation from the material level to the cell level Explains key battery basic lifetime test methods and strategies Presents advanced technologies of battery state of health estimation
Book Synopsis Predictive models of Li-ion battery lifetime by : Kandler Smith
Download or read book Predictive models of Li-ion battery lifetime written by Kandler Smith and published by . This book was released on 2014 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Lifetime prediction on lithium-ion battery cell and system level (Band 8) by : Severin Lukas Hahn
Download or read book Lifetime prediction on lithium-ion battery cell and system level (Band 8) written by Severin Lukas Hahn and published by Cuvillier Verlag. This book was released on 2022-08-23 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lithium-Ionen Batteriesysteme leiden unter elektrochemischen Degradations- und Ausfallmechanismen, die nur mit hohem Testaufwand abzusichern sind. Daher verfolgt diese Arbeit das Ziel, Prädiktionen des kalendarischen Kapazitätsverlustes und der Druckentwicklung auf Zell- und Systemebene zu verbessern. Eine fundamentale Inkonsistenz semi-empirischer kalendarischer Alterungsmodelle konnte aufgrund theoretischer Überlegungen aufgelöst werden, indem der Einfluss der initialen Anodendeckschicht berücksichtigt wird. Ein neuartiges Validierungskonzept, welches durch maschinelles Lernen inspiriert wurde, konnte die dadurch verbessere Prognosefähigkeit gegenüber der Literatur aufzeigen. Das Verhalten von Einzelzellen in repräsentativer Modulverspannung konnte auf einer neuen aktiv geregelte Zellpresse untersucht werden und schuf grundlegendes Verständnis. Die Presse ermöglichte damit die Systemmodellierung der Druckentwicklung, deren detaillierte Parametrisierung und die Messung des Gasverdrängungsdruckes von laminierten Zellen. Durch die Messung der Druckentwicklung in Alterungsversuchen von Modulen konnte die Modellprädiktion auf Systemebene erfolgreich für Moduldesigns validiert werden.
Book Synopsis Data-driven Diagnosis of Lithium-ion Battery Degradation Under Realistic Usage Conditions by : Devi Sribala Ganapathi
Download or read book Data-driven Diagnosis of Lithium-ion Battery Degradation Under Realistic Usage Conditions written by Devi Sribala Ganapathi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint by :
Download or read book Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint written by and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4 percent RMS error and resistance growth with 15 percent RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.
Book Synopsis Lithium-Ion Batteries Hazard and Use Assessment by : Celina Mikolajczak
Download or read book Lithium-Ion Batteries Hazard and Use Assessment written by Celina Mikolajczak and published by Springer Science & Business Media. This book was released on 2012-03-23 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lithium-Ion Batteries Hazard and Use Assessment examines the usage of lithium-ion batteries and cells within consumer, industrial and transportation products, and analyzes the potential hazards associated with their prolonged use. This book also surveys the applicable codes and standards for lithium-ion technology. Lithium-Ion Batteries Hazard and Use Assessment is designed for practitioners as a reference guide for lithium-ion batteries and cells. Researchers working in a related field will also find the book valuable.
Book Synopsis Degradation Mechanisms in NMC-Based Lithium-Ion Batteries by : Alexander Johannes Warnecke
Download or read book Degradation Mechanisms in NMC-Based Lithium-Ion Batteries written by Alexander Johannes Warnecke and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Studies on Fundamental Materials Degradation Mechanisms in Lithium-ion Batteries Via On-line Electrochemical Mass Spectrometry by : Michael Metzger
Download or read book Studies on Fundamental Materials Degradation Mechanisms in Lithium-ion Batteries Via On-line Electrochemical Mass Spectrometry written by Michael Metzger and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Characterization of Secondary Lithium-ion Battery Degradation when Operating Complex, Ultra-high Power Pulsed Loads by : Derek N. Wong
Download or read book The Characterization of Secondary Lithium-ion Battery Degradation when Operating Complex, Ultra-high Power Pulsed Loads written by Derek N. Wong and published by . This book was released on 2016 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The US Navy is actively developing all electric fleets, raising serious questions about what is required of onboard power supplies in order to properly power the ship's electrical systems. This is especially relevant when choosing a viable power source to drive high power propulsion and electric weapon systems in addition to the conventional loads deployed aboard these types of vessels. Especially when high pulsed power loads are supplied, the issue of maintaining power quality becomes important and increasingly complex. Conventionally, a vessel's electrical power is generated using gas turbine or diesel driven motor-generator sets that are very inefficient when they are used outside of their most efficient load condition. What this means is that if the generator is not being utilized continuously at its most efficient load capacity, the quality of the output power may also be effected and fall outside of the acceptable power quality limits imposed through military standards. As a solution to this potential problem, the Navy has proposed using electrochemical storage devices since they are able to buffer conventional generators when the load is operating below the generator's most efficient power level or able to efficiently augment a generator when the load is operating in excess of the generator's most efficient power rating. Specifically, the US Navy is interested in using commercial off-the-shelf (COTS) lithium-ion batteries within an intelligently controlled energy storage module that could act as either a prime power supply for on-board pulsed power systems or as a backup generator to other shipboard power systems. Due to the unique load profile of high-rate pulsed power systems, the implementation of lithium-ion batteries within these complex systems requires them to be operated at very high rates and the effects these things have on cell degradation has been an area of focus. There is very little published research into the effects that high power transient or pulsed loading has on the degradation mechanisms of secondary lithium-ion cells. Prior to performing this work, it was unclear if the implementation of lithium-ion batteries in highly transient load conditions at high rate would accelerate cell degradation mechanisms that have been previously considered as minor issues. This work has focused on answering these previously unanswered questions. In early experiments performed here, COTS lithium-iron-phosphate (LFP) cells were studied under high-rate, transient load conditions and it was found that their capacity fade deviated from the traditional linear behavior and exponentially declined until no charge could be accepted when recharge was attempted at high rate. These findings indicated that subjecting LFP chemistries to transient, high rate charge/discharge profiles induced rapid changes in the electrode/electrolyte interface that rendered the cells useless when high rate recharge was required. These findings suggested there was more phenomena to learn about how these cells degraded under high rate pulsed conditions before they are fielded in Naval applications. Therefore, the research presented here has been focused on understanding the degradation mechanisms that are unique to LFP cells when they are cycled under pulsed load profiles at high charge and discharge rates. In particular, the work has been focused on identifying major degradation reactions that occur by studying the surface chemistry of cycled electrode materials. Efforts have been performed to map the impedance evolution of both cathode and anode half cells, respectively, using a novel three electrode technique that was developed for this research. Using this technique, the progression of degradation has been mapped using analysis of differential capacitance spectrums. In both the three electrode EIS mapping and differential capacitance analysis that has been performed, electrical component models have been developed. The results presented will show that there are unique degradation mechanisms induced through high rate pulsed loading conditions that are not normally seen in low rate continuous cycling of LFP cells.
Book Synopsis Lifetime Prediction for Lithium-ion Batteries Undergoing Fast Charging Protocols by : Michael Forsuelo
Download or read book Lifetime Prediction for Lithium-ion Batteries Undergoing Fast Charging Protocols written by Michael Forsuelo and published by . This book was released on 2019 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes the application of Porous Electrode Theory and supervised machine learning to lifetime prediction for 18650 lithium iron phosphate (LiFePO4 LFP)/graphite cells subject to mixed galvanostatic and potentiostatic fast charging policies. Porous Electrode Theory is used to predict battery lifetime by parameteric reductions of effective solid-phase Fickian diffusivities, electrolytic Stefan-Maxwell diffusivity, and Butler-Volmer exchange currents. Parameter estimation and uncertainty quantification are formulated as least squares optimization over galvanostatic discharge curves with Bayesian estimation of uncertainties. A battery lifetime approach from the literature is extended with identifiability analysis to enhance fidelity of the inverse problem, the attribution of degradation modes, and the accuracy of parametric power-law lifetime predictions. Multiphase Porous Electrode Theory (MPET) is also explored in this thesis. In MPET, each particle of the porous electrode ensemble is described by generalized Allen-Cahn-Hilliard dynamics. Single-particle dynamics are governed by firstprinciples free energy landscapes as opposed to inductive fits to open-circuit battery voltages. Multiscale parameter estimation and central limit theorem analysis are implemented, enhancing the suitability of MPET for capacity fade predictions. Supervised machine learning algorithms utilizing feature-based correlations for battery lifetime are described. Electrochemical features that go beyond the discharge-only model provide improved lifetime predictions, generalized voltage analysis indiscrimant of (dis)charge protocol or data, and a clear connection between battery physics and machine learning, and suggest an optimal charging protocol.
Book Synopsis Experimental Aging and Lifetime Prediction in Grid Applications for Large-Format Commercial Li-Ion Batteries by :
Download or read book Experimental Aging and Lifetime Prediction in Grid Applications for Large-Format Commercial Li-Ion Batteries written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growth of electric vehicle and stationary energy storage markets, the production and use of lithium-ion batteries has grown exponentially in recent years. For many of these applications, large-format lithium-ion batteries are being utilized, as large cells have less inactive material relative to their energy capacity and require fewer electrical connections to assemble into packs. And especially for stationary energy storage systems, where energy delivered is the only revenue source, the economics of these battery systems is highly dependent on cell lifetime. However, testing of large-format lithium-ion batteries is time consuming and requires high current channels and large testing chambers, making information on the performance of commercial, large-format lithium-ion batteries hard to come by. Here, accelerated aging test data from four commercial large-format lithium-ion batteries is reported. These batteries span both NMC-Gr and LFP-Gr cell chemistries, pouch and prismatic formats, and a range of cell designs with varying power capabilities. Accelerated aging test results are analyzed to examine both cell performance, in terms of efficiency and thermal response under load, as well as cell lifetime. Cell thermal response is characterized by measuring temperature during cycle aging, which is used to calculated a normalized thermal resistance value that may help estimate both cell cooling needs or to help extrapolate aging test results to different thermal environments. Cell lifetime is evaluated qualitatively, considering simply the average calendar and cycle life across a range of conditions, as well as quantitatively, using statistical modeling and machine-learning methods to identify predictive aging models from the accelerated aging data. These predictive aging models are then used to investigate cell sensitivities to stressors, such as cycling temperature, voltage window, and C-rate, as well as to predict cell lifetime in various stationary storage applications. Results from this work show that cell lifetime and sensitivity to aging conditions varies substantially across commercial cells, necessitating testing for specific cell formats to make quantitative lifetime predictions. That being said, all commercial cells tested here are predicted to reach at least 10-year lifetimes for stationary storage applications. Based on the aging test results and modeling, some cells are expected to be relatively insensitive to temperature and use-case, making them suited for simple use cases with little or no thermal management and simple controls, while the lifetime of other cells could be extended to 20+ years if operated with thermal management and degradation-aware controls.
Book Synopsis Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation). by :
Download or read book Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation). written by and published by . This book was released on 2014 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.
Book Synopsis Determination of Degradation Mechanisms During the Cyclic Ageing of Li-ion Batteries by : Matthias Simolka
Download or read book Determination of Degradation Mechanisms During the Cyclic Ageing of Li-ion Batteries written by Matthias Simolka and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Predictive Models of Li-ion Battery Lifetime by :
Download or read book Predictive Models of Li-ion Battery Lifetime written by and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: It remains an open question how best to predict real-world battery lifetime based on accelerated calendar and cycle aging data from the laboratory. Multiple degradation mechanisms due to (electro)chemical, thermal, and mechanical coupled phenomena influence Li-ion battery lifetime, each with different dependence on time, cycling and thermal environment. The standardization of life predictive models would benefit the industry by reducing test time and streamlining development of system controls.
Book Synopsis Degradation Mechanisms of High-energy Electrode Materials for Lithium-ion Batteries by : Roland Jung
Download or read book Degradation Mechanisms of High-energy Electrode Materials for Lithium-ion Batteries written by Roland Jung and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Model Based Optimal Control, Estimation, and Validation of Lithium-Ion Batteries by : Hector Eduardo Perez
Download or read book Model Based Optimal Control, Estimation, and Validation of Lithium-Ion Batteries written by Hector Eduardo Perez and published by . This book was released on 2016 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on developing and experimentally validating model based control techniques to enhance the operation of lithium ion batteries, safely. An overview of the contributions to address the challenges that arise are provided below. Chapter 1: This chapter provides an introduction to battery fundamentals, models, and control and estimation techniques. Additionally, it provides motivation for the contributions of this dissertation. Chapter 2: This chapter examines reference governor (RG) methods for satisfying state constraints in Li-ion batteries. Mathematically, these constraints are formulated from a first principles electrochemical model. Consequently, the constraints explicitly model specific degradation mechanisms, such as lithium plating, lithium depletion, and overheating. This contrasts with the present paradigm of limiting measured voltage, current, and/or temperature. The critical challenges, however, are that (i) the electrochemical states evolve according to a system of nonlinear partial differential equations, and (ii) the states are not physically measurable. Assuming available state and parameter estimates, this chapter develops RGs for electrochemical battery models. The results demonstrate how electrochemical model state information can be utilized to ensure safe operation, while simultaneously enhancing energy capacity, power, and charge speeds in Li-ion batteries. Chapter 3: Complex multi-partial differential equation (PDE) electrochemical battery models are characterized by parameters that are often difficult to measure or identify. This parametric uncertainty influences the state estimates of electrochemical model-based observers for applications such as state-of-charge (SOC) estimation. This chapter develops two sensitivity-based interval observers that map bounded parameter uncertainty to state estimation intervals, within the context of electrochemical PDE models and SOC estimation. Theoretically, this chapter extends the notion of interval observers to PDE models using a sensitivity-based approach. Practically, this chapter quantifies the sensitivity of battery state estimates to parameter variations, enabling robust battery management schemes. The effectiveness of the proposed sensitivity-based interval observers is verified via a numerical study for the range of uncertain parameters. Chapter 4: This chapter seeks to derive insight on battery charging control using electrochemistry models. Directly using full order complex multi-partial differential equation (PDE) electrochemical battery models is difficult and sometimes impossible to implement. This chapter develops an approach for obtaining optimal charge control schemes, while ensuring safety through constraint satisfaction. An optimal charge control problem is mathematically formulated via a coupled reduced order electrochemical-thermal model which conserves key electrochemical and thermal state information. The Legendre-Gauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting nonlinear multi-state optimal control problem. Minimum time charge protocols are analyzed in detail subject to solid and electrolyte phase concentration constraints, as well as temperature constraints. The optimization scheme is examined using different input current bounds, and an insight on battery design for fast charging is provided. Experimental results are provided to compare the tradeoffs between an electrochemical-thermal model based optimal charge protocol and a traditional charge protocol. Chapter 5: Fast and safe charging protocols are crucial for enhancing the practicality of batteries, especially for mobile applications such as smartphones and electric vehicles. This chapter proposes an innovative approach to devising optimally health-conscious fast-safe charge protocols. A multi-objective optimal control problem is mathematically formulated via a coupled electro-thermal-aging battery model, where electrical and aging sub-models depend upon the core temperature captured by a two-state thermal sub-model. The Legendre-Gauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting highly nonlinear six-state optimal control problem. Charge time and health degradation are therefore optimally traded off, subject to both electrical and thermal constraints. Minimum-time, minimum-aging, and balanced charge scenarios are examined in detail. Sensitivities to the upper voltage bound, ambient temperature, and cooling convection resistance are investigated as well. Experimental results are provided to compare the tradeoffs between a balanced and traditional charge protocol. Chapter 6: This chapter provides concluding remarks on the findings of this dissertation and a discussion of future work.
