Effect of Design Parameters and Intercalation Induced Stresses in Lithium Ion Batteries

Electrochemical power sources, especially lithium ion batteries have become major players in various industrial sectors, with applications ranging from low power/energy demands to high power/energy requirements. But there are some significant issues existing for lithium ion systems which include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. Therefore, better design, operation and control of lithium ion batteries are essential to meet the growing demands of energy storage. Physics based modeling and simulation methods provide the best and most accurate approach for addressing such issues for lithium ion battery systems. This work tries to understand and address some of these issues, by development of physics based models and efficient simulation of such models for battery design and real time control purposes. This thesis will introduce a model-based procedure for simultaneous optimization of design parameters for porous electrodes that are commonly used in lithium ion systems. The approach simultaneously optimizes the battery design variables of electrode porosities and thickness for maximization of the energy drawn for an applied current, cut-off voltage, and total time of discharge. The results show reasonable improvement in the specific energy drawn from the lithium ion battery when the design parameters are simultaneously optimized. The second part of this dissertation will develop a 2-dimensional transient numerical model used to simulate the electrochemical lithium insertion in a silicon nanowire (Si NW) electrode. The model geometry is a cylindrical Si NW electrode anchored to a copper current collector (Cu CC) substrate. The model solves for diffusion of lithium in Si NW, stress generation in the Si NW due to chemical and elastic strain, stress generation in the Cu CC due to elastic strain, and volume expansion in the Si NW and Cu CC geometries. The evolution of stress components, i.e., radial, axial and tangential stresses in different regions in the Si NW are studied in details. Lithium-ion batteries are typically modeled using porous electrode theory coupled with various transport and reaction mechanisms with an appropriate discretization or approximation for the solid phase diffusion within the electrode particle. One of the major difficulties in simulating Li-ion battery models is the need for simulating solid-phase diffusion in the second radial dimension r within the particle. It increases the complexity of the model as well as the computation time/cost to a great extent. This is particularly true for the inclusion of pressure induced diffusion inside particles experiencing volume change. Therefore, to address such issues, part of the work will involve development of efficient methods for particle/solid phase reformulation - (1) parabolic profile approach and (2) a mixed order finite difference method. These models will be used for approximating/representing solid-phase concentration variations within the active material. Efficiency in simulation of particle level models can be of great advantage when these are coupled with macro-homogenous cell sandwich level battery models

Capacity Fade Analysis and Model Based Optimization of Lithium-ion Batteries

Electrochemical power sources have had significant improvements in design, economy, and operating range and are expected to play a vital role in the future in a wide range of applications. The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This dissertation contributes to the efforts in the modeling, simulation and optimization of lithium-ion batteries and their use in the design of better batteries for the future. While physics-based models have been widely developed and studied for these systems, the rigorous models have not been employed for parameter estimation or dynamic optimization of operating conditions. The first chapter discusses a systems engineering based approach to illustrate different critical issues possible ways to overcome them using modeling, simulation and optimization of lithium-ion batteries. The chapters 2-5, explain some of these ways to facilitate: i) capacity fade analysis of Li-ion batteries using different approaches for modeling capacity fade in lithium-ion batteries,: ii) model based optimal design in Li-ion batteries and: iii) optimum operating conditions: current profile) for lithium-ion batteries based on dynamic optimization techniques. The major outcomes of this thesis will be,: i) comparison of different types of modeling efforts that will help predict and understand capacity fade in lithium-ion batteries that will help design better batteries for the future,: ii) a methodology for the optimal design of next-generation porous electrodes for lithium-ion batteries, with spatially graded porosity distributions with improved energy efficiency and battery lifetime and: iii) optimized operating conditions of batteries for high energy and utilization efficiency, safer operation without thermal runaway and longer life

Novel Approaches for State of Charge Modeling in Battery Management Systems

One of the key steps of any battery management system design is the representation of the open circuit voltage (OCV) as a function of the state of charge (SOC). The OCV-SOC relationship is very non-linear that is often represented using a polynomial that has log and inverse terms that are not defined around SOC equal to zero or one. The traditional response to this problem was only at the software level. In this thesis, I present a formal scaling approach to the OCV-SOC characterization in Li-ion batteries. I show that, through formal modeling and optimization, the traditional approach to OCV-SOC modeling can be significantly improved by selecting the proper value of $\epsilon$. When the proposed technique is used a decrease in the maximum SOC error of 9\% is reported. The proposed approach is tested on data collected from multiple cells over various temperatures for OCV-SOC characterization and the results are presented. State-space model (SSM) and the Kalman filter have several applications in the emerging areas of automation and data science including in battery SOC estimation. In many such applications, the application of Kalman filtering requires model identification with the help of the observed data. I present the formulas with derivations for linear state-space model parameter estimation using the expectation maximization (EM) algorithm. Particularly, I derive the formulas for different special SSM cases of practical interest, such as the continuous white noise acceleration (CWNA) model. Through simulation, I show the benefits of these derivations for the special models in comparison with the generalized approach

Optimal Battery Operations and Design Considering Capacity Fade Mechanisms

Safely and capacity fade are the key issues that restrict the use of the lithium-ion battery for many applications. These issues are being tackled in a variety of ways. This dissertation focuses on using detailed continuum-level electrochemical models to study transport, kinetics, and mechanical processes in the lithium-ion batteries. These models can be used to quantify the effect of capacity fade mechanisms (side reactions and mechanical degradation) and improve the safety aspects of the lithium ion batteries. Three capacity-fade mechanisms—solid electrolyte interface side reaction, lithium-plating side reaction and mechanical degradation due to intercalation-induced stresses—are considered in the dissertation. Monitoring and control of plating side reaction is also very critical for battery safety. Two main focus areas of the dissertation are: 1) Optimal battery operation (design of charging/discharging protocols) considering three capacity fade mechanisms mentioned previously along with safety issues 2) Rational battery design (choice of porosity, thicknesses of electrodes, etc.) considering discharge capacity and capacity fade mechanism

Integration, control and optimization of the solar photovoltaic-battery system in microgrids

This document composes the work realised and the research results developed within the scope of electric energy storage at the Renewable Energy Chair of the University of Évora. The current legal and technological framework of electrochemical energy storage technologies is reported, and its framework is demonstrated in the Portuguese and European contexts. Next, the experimental microgrid that comprises several electric energy storage technologies is described. The lithium-ion and vanadium redox flow technologies were tested and characterized for later validation of the electrical models that describe their performance. A state-of-the-art review allowed the experimentation of energy management strategies that fit the technologies studied, allowing smarter management in residential and services sectors. In this thesis, management algorithms, battery models, and an indication of technical, economic and energy parameters were combined in a tool to study the simulation of the operation of these technologies, allowing to define different operating objectives, fine-tune parameters and even join the operation of different technologies. This work was accompanied by national and international projects, attempting to respond to existing problems in the operation of real systems and gaps identified in the design phase, such as a robust dimensioning tool, with the integration of different battery managing methods; Integração, controlo e otimização do sistema solar fotovoltaico-bateria em microrredes Resumo: Este documento compõe o trabalho realizado e respetivos resultados da investigação desenvolvida no âmbito do armazenamento de energia elétrica na Cátedra Energias Renováveis da Universidade de Évora. Os atuais enquadramentos legais e tecnológicos das tecnologias eletroquímicas de armazenamento de energia são relatados, nos contextos português e europeu. Seguidamente, uma microrrede experimental que inclui diversas tecnologias de armazenamento de energia elétrica é descrita. As tecnologias de fluxo redox de vanádio e de iões de lítio foram objeto de ensaio e caracterização, para posterior validação dos correspondentes modelos que descrevem a sua performance elétrica. A revisão do estado da arte permitiu a experimentação de estratégias de gestão de energia que se adequam às tecnologias estudadas, que permitam a sua gestão inteligente, no contexto residencial e de serviços. Nesta tese, os algoritmos de gestão, os modelos das baterias, a indicação de parâmetros técnicos, económicos e energéticos foram combinados numa ferramenta para estudo da simulação da operação destas tecnologias permitindo definir diferente objetivos, afinar parâmetros e até operar conjuntamente diferentes tecnologias. Este trabalho foi acompanhado pelo paralelismo de projetos nacionais e internacionais, tentado dar resposta a problemas existentes na operação de sistemas reais, e lacunas identificadas na fase de projeto, tal como uma ferramenta de dimensionamento robusto, com a integração de diferentes formas de gerir baterias

E-transportation: the role of embedded systems in electric energy transfer from grid to vehicle

Electric vehicles (EVs) are a promising solution to reduce the transportation dependency on oil, as well as the environmental concerns. Realization of E-transportation relies on providing electrical energy to the EVs in an effective way. Energy storage system (ESS) technologies, including batteries and ultra-capacitors, have been significantly improved in terms of stored energy and power. Beside technology advancements, a battery management system is necessary to enhance safety, reliability and efficiency of the battery. Moreover, charging infrastructure is crucial to transfer electrical energy from the grid to the EV in an effective and reliable way. Every aspect of E-transportation is permeated by the presence of an intelligent hardware platform, which is embedded in the vehicle components, provided with the proper interfaces to address the communication, control and sensing needs. This embedded system controls the power electronics devices, negotiates with the partners in multi-agent scenarios, and performs fundamental tasks such as power flow control and battery management. The aim of this paper is to give an overview of the open challenges in E-transportation and to show the fundamental role played by embedded systems. The conclusion is that transportation electrification cannot fully be realized without the inclusion of the recent advancements in embedded systems

Optimal porosity distribution for minimized ohmic drop across a porous electrode

This paper considers the design of spatially varying porosity profiles in next-generation electrodes based on simultaneous optimization of a porous-electrode model. Model-based optimal design ͑not including the solid-phase intercalation mechanism͒ is applied to a porous positive electrode made of lithium cobalt oxide, which is commonly used in lithium-ion batteries for various applications. For a fixed amount of active material, optimal grading of the porosity across the electrode was found to decrease the ohmic resistance by 15%-33%, which in turn increases the electrode capacity to hold and deliver energy. The optimal porosity grading was predicted to have 40% lower variation in the ohmic resistance to variations in model parameters due to manufacturing imprecision or capacity fade. The results suggest that the potential for the simultaneous model-based design of electrode material properties that employ more detailed physics-based first-principles electrochemical engineering models to determine optimal design values for manufacture and experimental evaluation. © 2010 The Electrochemical Society. ͓DOI: 10.1149/1.3495992͔ All rights reserved. Electrochemical power sources have had significant improvements in design and operating range and are expected to play a vital role in the future in automobiles, power storage, military, and space applications. Lithium-ion chemistry has been identified as a preferred candidate for high-power/high-energy secondary batteries. Applications for batteries range from implantable cardiovascular defibrillators operating at 10 A current to hybrid vehicles requiring pulses of up to 100 A. Today, the design of these systems have been primarily based on ͑i͒ matching the capacity of anode and cathode materials; ͑ii͒ trial-and-error investigation of thickness, porosity, active material, and additive loading; ͑iii͒ manufacturing convenience and cost; ͑iv͒ ideal expected thermal behavior at the system level to handle high currents; and ͑v͒ detailed microscopic models to understand, optimize, and design these systems by changing one or few parameters at a time. Traditionally, macroscopic models have been used to optimize the electrode thickness or spatially uniform porosity in lithium-ion battery design. Many applications of mathematical modeling to design Li-ion batteries are available in the literature. 1-10 An approach to identify the optimal values of system parameters such as electrode thickness has been reported by Newman and co-workers. 2,5-10 Simplified models based on porous-electrode theory can provide analytical expressions to describe the discharge of rechargeable lithium-ion batteries in terms of the relevant system parameters. Newman and co-workers 2,5-8 have utilized continuum electrochemical engineering models for design and optimization as a tool for the identification of system limitations from the experimental data. Equations were developed that describe the time dependence of potential as a function of electrode porosity and thickness, the electrolyte and solid-phase conductivities, specific ampere-hour capacity, separator conductivity and thickness, and current density. Analysis of these equations yields the values of electrode porosity and electrode thickness so as to maximize the capacity for discharge to a given cutoff potential. Simplified models based on porous-electrode theory were used to describe the discharge of rechargeable lithium batteries and derive analytical expressions for the cell potential, specific energy, and average power in terms of the relevant system parameters. The resulting theoretical expressions were used for design and optimization purposes and for the identification of system limitations from experimental data. 5 Studies were performed by comparing the Ragone plots for a range of design parameters. A single curve in a Ragone plot involves hundreds of simulations wherein the applied current is varied over a wide range of magnitude. Ragone plots for different configurations are obtained by changing the design parameters ͑e.g., thickness͒ one at a time and by keeping the other parameters at constant values. This process of generating a Ragone plot is quite tedious, and typically Ragone curves reported in the literature are not smooth due to computational constraints. Batteries are typically designed only to optimize the performance at the very first cycle of operation of the battery, whereas in practice most of the battery's operation occurs under significantly degraded conditions. Further, multivariable optimization is not computationally efficient using most first-principles models described in the literature. A reformulated model Electrochemical Porous-Electrode Model Garcia et al. 14 provided a framework for modeling microstructural effects in electrochemical devices. That model can be extended to treat more complex microstructures and physical phenomena such as particle distributions, multiple electrode phase mixtures, phase transitions, complex particle shapes, and anisotropic solid-state diffusivities. As mentioned earlier, there are several treatments fo

Optimal porosity distribution for minimized ohmic drop across a porous electrode

This paper considers the design of spatially varying porosity profiles in next-generation electrodes based on simultaneous optimization of a porous-electrode model. Model-based optimal design ͑not including the solid-phase intercalation mechanism͒ is applied to a porous positive electrode made of lithium cobalt oxide, which is commonly used in lithium-ion batteries for various applications. For a fixed amount of active material, optimal grading of the porosity across the electrode was found to decrease the ohmic resistance by 15%-33%, which in turn increases the electrode capacity to hold and deliver energy. The optimal porosity grading was predicted to have 40% lower variation in the ohmic resistance to variations in model parameters due to manufacturing imprecision or capacity fade. The results suggest that the potential for the simultaneous model-based design of electrode material properties that employ more detailed physics-based first-principles electrochemical engineering models to determine optimal design values for manufacture and experimental evaluation. © 2010 The Electrochemical Society. ͓DOI: 10.1149/1.3495992͔ All rights reserved. Electrochemical power sources have had significant improvements in design and operating range and are expected to play a vital role in the future in automobiles, power storage, military, and space applications. Lithium-ion chemistry has been identified as a preferred candidate for high-power/high-energy secondary batteries. Applications for batteries range from implantable cardiovascular defibrillators operating at 10 A current to hybrid vehicles requiring pulses of up to 100 A. Today, the design of these systems have been primarily based on ͑i͒ matching the capacity of anode and cathode materials; ͑ii͒ trial-and-error investigation of thickness, porosity, active material, and additive loading; ͑iii͒ manufacturing convenience and cost; ͑iv͒ ideal expected thermal behavior at the system level to handle high currents; and ͑v͒ detailed microscopic models to understand, optimize, and design these systems by changing one or few parameters at a time. Traditionally, macroscopic models have been used to optimize the electrode thickness or spatially uniform porosity in lithium-ion battery design. Many applications of mathematical modeling to design Li-ion batteries are available in the literature. 1-10 An approach to identify the optimal values of system parameters such as electrode thickness has been reported by Newman and co-workers. 2,5-10 Simplified models based on porous-electrode theory can provide analytical expressions to describe the discharge of rechargeable lithium-ion batteries in terms of the relevant system parameters. Newman and co-workers 2,5-8 have utilized continuum electrochemical engineering models for design and optimization as a tool for the identification of system limitations from the experimental data. Equations were developed that describe the time dependence of potential as a function of electrode porosity and thickness, the electrolyte and solid-phase conductivities, specific ampere-hour capacity, separator conductivity and thickness, and current density. Analysis of these equations yields the values of electrode porosity and electrode thickness so as to maximize the capacity for discharge to a given cutoff potential. Simplified models based on porous-electrode theory were used to describe the discharge of rechargeable lithium batteries and derive analytical expressions for the cell potential, specific energy, and average power in terms of the relevant system parameters. The resulting theoretical expressions were used for design and optimization purposes and for the identification of system limitations from experimental data. 5 Studies were performed by comparing the Ragone plots for a range of design parameters. A single curve in a Ragone plot involves hundreds of simulations wherein the applied current is varied over a wide range of magnitude. Ragone plots for different configurations are obtained by changing the design parameters ͑e.g., thickness͒ one at a time and by keeping the other parameters at constant values. This process of generating a Ragone plot is quite tedious, and typically Ragone curves reported in the literature are not smooth due to computational constraints. Batteries are typically designed only to optimize the performance at the very first cycle of operation of the battery, whereas in practice most of the battery's operation occurs under significantly degraded conditions. Further, multivariable optimization is not computationally efficient using most first-principles models described in the literature. A reformulated model Electrochemical Porous-Electrode Model Garcia et al. 14 provided a framework for modeling microstructural effects in electrochemical devices. That model can be extended to treat more complex microstructures and physical phenomena such as particle distributions, multiple electrode phase mixtures, phase transitions, complex particle shapes, and anisotropic solid-state diffusivities. As mentioned earlier, there are several treatments fo

Model-free non-invasive health assessment for battery energy storage assets

Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health.Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health