Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source

A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation

The final publication is available at Elsevier via http://doi.org/10.1016/j.jpowsour.2014.01.057 © 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, we survey two kinds of mathematics-based battery models intended for use in hybrid and electric vehicle simulation. The first is circuit-based, which is founded upon the electrical behaviour of the battery, and abstracts away the electrochemistry into equivalent electrical components. The second is chemistry-based, which is founded upon the electrochemical equations of the battery chemistry.Natural Sciences and Engineering Research Council (NSERC) of Canada, Toyota, and MapleSoft

Kalman-variant estimators for state of charge in lithium-sulfur batteries

Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort

Efficient electrochemical model for lithium-ion cells

Lithium-ion batteries are used to store energy in electric vehicles. Physical models based on electro-chemistry accurately predict the cell dynamics, in particular the state of charge. However, these models are nonlinear partial differential equations coupled to algebraic equations, and they are computationally intensive. Furthermore, a variable solid-state diffusivity model is recommended for cells with a lithium ion phosphate positive electrode to provide more accuracy. This variable structure adds more complexities to the model. However, a low-order model is required to represent the lithium-ion cells' dynamics for real-time applications. In this paper, a simplification of the electrochemical equations with variable solid-state diffusivity that preserves the key cells' dynamics is derived. The simplified model is transformed into a numerically efficient fully dynamical form. It is proved that the simplified model is well-posed and can be approximated by a low-order finite-dimensional model. Simulations are very quick and show good agreement with experimental data

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

Modeling and control of fuel cell-battery hybrid energy sources

Environmental, political, and availability concerns regarding fossil fuels in recent decades have garnered substantial research and development in the area of alternative energy systems. Among various alternative energy systems, fuel cells and batteries have attracted significant attention both in academia and industry considering their superior performances and numerous advantages. In this dissertation, the modeling and control of these two electrochemical sources as the main constituents of fuel cell-battery hybrid energy sources are studied with ultimate goals of improving their performance, reducing their development and operational costs and consequently, easing their widespread commercialization. More specifically, Paper I provides a comprehensive background and literature review about Li-ion battery and its Battery Management System (BMS). Furthermore, the development of an experimental BMS design testbench is introduced in this paper. Paper II discusses the design of a novel observer for Li-ion battery State of Charge (SOC) estimation, as one of the most important functionalities of BMSs. Paper III addresses the control-oriented modeling and analysis of open-cathode fuel cells in order to provide a comprehensive system-level understanding of their real-time operation and to establish a basis for control design. Finally, in Paper IV a feedback controller, combined with a novel output-injection observer, is designed and implemented for open-cathode fuel cell temperature control. It is shown that temperature control not only ensures the fuel cell temperature reference is properly maintained, but, along with an uncertainty estimator, can also be used to adaptively stabilize the output voltage --Abstract, page iv

A New Multiscale Modeling Framework for Lithium-Ion Battery Dynamics: Theory, Experiments, and Comparative Study with the Doyle-Fuller-Newman Model

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

Understanding and modelling the thermal behaviour of incumbent and future lithium ion batteries

The thesis begins with a literature review on the thermal behaviours for an incumbent and a future lithium ion battery, which are Lithium iron phosphate (LFP) prismatic batteries and Lithium sulfur (Li-S) pouch batteries, respectively. Research gaps were identified for both types of batteries, requiring the development of novel experimental techniques and/or modelling approaches for each type. Lithium sulfur batteries are an important next generation high energy density battery technology. However, the phenomenon known as the polysulfide shuttle was identified as one of the most important challenges needing to be overcome. It causes accelerated degradation, reduced Coulombic efficiency and increased heat generation, particularly towards the end of charge. Research was conducted on how to track, quantify and therefore prevent the shuttle effect, in order to improve the safety and increase cycle life of Li-S batteries in real applications. This required the real-time detection of the onset of shuttle during charge. The diagnostic technique Differential Thermal Voltammetry (DTV) was used to track the shuttle effect during charging for the first time, and quantitative interpretations of the experimental DTV curves were performed by thermally-coupling a zero-dimensional Li-S model. The DTV technique, together with the model, is a promising tool for real-time detection of shuttle in applications, to inform control algorithms for deciding the end of charging, thus preventing excessive degradation and charge inefficiency. Lithium iron phosphate prismatic batteries are widely used in both sustainable transportation and stationary energy storage. However, system level thermal management for large format prismatic cells is rarely considered in the literature. Equivalent circuit models (ECM) were shortlisted, due to their ease of implementation and low complexity. The accuracy of an ECM is critical to the functionality and usefulness of the battery management system (BMS). However, their accuracy is limited by how easy they are parameterised, and therefore different experimental techniques and model parameter identification methods (PIM) have been widely studied. Yet, how to account for significant changes in time constants between operation under load and during relaxation has not been resolved. In this work a novel PIM and a modified ECM is presented that increases accuracy by 77.4% during drive cycle validation and 87.6% during constant current load validation for a large format LFP prismatic cell. The modified ECM uses switching RC network values for each phase, which is significant for this cell and particularly at low state-of-charge for all lithium ion batteries. Different characterisation tests and the corresponding experimental data have been trained together across a complete State-of-Charge (SoC) and temperature range, which enables a smooth transition between identified parameters. Ultimately, the model created using parameters captured by the proposed PIM shows an improved model accuracy in comparison with conventional PIM techniques. Large format prismatic cell’s thermal management is challenging due to the large internal heat generation rate, longer distance for internal battery core away from the heat exchange cooling interface and therefore larger thermal gradient across the cell. The standardised surface Cell Cooling Coefficient (CCC) can be used to quantify the degree of difficulty of a target cell to be thermally managed. Here, in this thesis, the novel metric surface CCC is introduced and implemented onto a large format LFP prismatic cell, with aluminium alloy prismatic casing. Further, based on developed PIM, a parameterised and discretised 3-dimentional Electro-Thermal Equivalent Circuit Model is developed. The developed model is validated using the experimental data through embedding corresponding boundary conditions, including drive cycle noisy load and constant current CCC square wave load, electrically and thermally at the same time. The study offers a quantitative guide of the trade-off between cell energy density and surface CCC, and also a casing selection analysis is conducted. The CCC metric together with proposed model enable the cell manufacturer and Original Equipment Makers (OEMs) to customise the cell design based on the casing material, single cell energy density, cell thickness and CCC/capability to be thermally managed. In the future cell design process, this study offers a cost-effective, time-efficient, convenient and quantitative way, in order to achieve a better and safe battery design (high capacity, power and longer lifetime) for wider application needs. Finally, it is concluded that, for both incumbent and future lithium ion batteries, understanding the thermal behaviour is the key for a safer, lighter, longer lifetime, longer range application. By using engineering customised experimental techniques together with empirical and/or physical simulations, enhanced understanding with quantitative battery optimisation and thermal management are achieved in this thesis. The findings in thesis are beneficial for wide range of communities including research community, industry OEMs, application engineers, battery management system developers, control engineers and electric vehicle end users.Open Acces

Contributions on DC microgrid supervision and control strategies for efficiency optimization through battery modeling, management, and balancing techniques

Aquesta tesi presenta equips, models i estratègies de control que han estat desenvolupats amb l'objectiu final de millorar el funcionament d'una microxarxa CC. Es proposen dues estratègies de control per a millorar l'eficiència dels convertidors CC-CC que interconnecten les unitats de potència de la microxarxa amb el bus CC. La primera estratègia, Control d'Optimització de Tensió de Bus centralitzat, administra la potència del Sistema d'Emmagatzematge d'Energia en Bateries de la microxarxa per aconseguir que la tensió del bus segueixi la referència dinàmica de tensió òptima que minimitza les pèrdues dels convertidors. La segona, Optimització en Temps Real de la Freqüència de Commutació, consisteix a operar localment cada convertidor a la seva freqüència de commutació òptima, minimitzant les seves pèrdues. A més, es proposa una nova topologia d'equilibrador actiu de bateries mitjançant un únic convertidor CC-CC i s'ha dissenyat la seva estratègia de control. El convertidor CC-CC transfereix càrrega cel·la a cel·la, emprant encaminament de potència a través d'un sistema d'interruptors controlats. L'estratègia de control de l'equalitzador aconsegueix un ràpid equilibrat del SOC evitant sobrecompensar el desequilibri. Finalment, es proposa un model simple de degradació d'una cel·la NMC amb elèctrode negatiu de grafit. El model combina la simplicitat d'un model de circuit equivalent, que explica la dinàmica ràpida de la cel·la, amb un model físic del creixement de la capa Interfase Sòlid-Electròlit (SEI), que prediu la pèrdua de capacitat i l'augment de la resistència interna a llarg termini. El model proposat quantifica la incorporació de liti al rang de liti ciclable necessària per a aconseguir els límits de OCV després de la pèrdua de liti ciclable en la reacció secundària. El model de degradació SEI pot emprar-se per a realitzar un control predictiu de bateries orientat a estendre la seva vida útil.Aquesta tesi presenta equips, models i estratègies de control que han estat desenvolupats amb l'objectiu final de millorar el funcionament d'una microxarxa CC. Es proposen dues estratègies de control per a millorar l'eficiència dels convertidors CC-CC que interconnecten les unitats de potència de la microxarxa amb el bus CC. La primera estratègia, Control d'Optimització de Tensió de Bus centralitzat, administra la potència del Sistema d'Emmagatzematge d'Energia en Bateries de la microxarxa per aconseguir que la tensió del bus segueixi la referència dinàmica de tensió òptima que minimitza les pèrdues dels convertidors. La segona, Optimització en Temps Real de la Freqüència de Commutació, consisteix a operar localment cada convertidor a la seva freqüència de commutació òptima, minimitzant les seves pèrdues. A més, es proposa una nova topologia d'equilibrador actiu de bateries mitjançant un únic convertidor CC-CC i s'ha dissenyat la seva estratègia de control. El convertidor CC-CC transfereix càrrega cel·la a cel·la, emprant encaminament de potència a través d'un sistema d'interruptors controlats. L'estratègia de control de l'equalitzador aconsegueix un ràpid equilibrat del SOC evitant sobrecompensar el desequilibri. Finalment, es proposa un model simple de degradació d'una cel·la NMC amb elèctrode negatiu de grafit. El model combina la simplicitat d'un model de circuit equivalent, que explica la dinàmica ràpida de la cel·la, amb un model físic del creixement de la capa Interfase Sòlid-Electròlit (SEI), que prediu la pèrdua de capacitat i l'augment de la resistència interna a llarg termini. El model proposat quantifica la incorporació de liti al rang de liti ciclable necessària per a aconseguir els límits de OCV després de la pèrdua de liti ciclable en la reacció secundària. El model de degradació SEI pot emprar-se per a realitzar un control predictiu de bateries orientat a estendre la seva vida útil.This dissertation presents a set of equipment, models and control strategies, that have been developed with the final goal of improving the operation of a DC microgrid. Two control strategies are proposed to improve the efficiency of the DC-DC converters that interface the microgrid’s power units with the DC bus. The first strategy is centralized Bus Voltage Optimization Control, which manages the power of the microgrid’s Battery Energy Storage System to make the bus voltage follow the optimum voltage dynamic reference that minimizes the converters’ losses. The second control strategy is Online Optimization of Switching Frequency, which consists in locally operating each converter at its optimum switching frequency, again minimizing power losses. The two proposed optimization strategies have been validated in simulations. Moreover, a new converter-based active balancing topology has been proposed and its control strategy has been designed. This equalizer topology consists of a single DC-DC converter that performs cell-to-cell charge transfer employing power routing via controlled switches. The control strategy of the equalizer has been designed to achieve rapid SOC balancing while avoiding imbalance overcompensation. Its performance has been validated in simulation. Finally, a simple degradation model of an NMC battery cell with graphite negative electrode is proposed. The model combines the simplicity of an equivalent circuit model, which explains the fast dynamics of the cell, with a physical model of the Solid-Electrolyte Interphase (SEI) layer growth process, which predicts the capacity loss and the internal resistance rise in the long term. The proposed model fine-tunes the capacity loss prediction by accounting for the incorporation of unused lithium reserves of both electrodes into the cyclable lithium range to reach the OCV limits after the side reaction has consumed cyclable lithium. The SEI degradation model can be used to perform predictive control of batteries oriented toward extending their lifetime

Towards Better Understanding of Failure Modes in Lithium-Ion Batteries: Design for Safety

In this digital age, energy storage technologies become more sophisticated and more widely used as we shift from traditional fossil fuel energy sources to renewable solutions. Specifically, consumer electronics devices and hybrid/electric vehicles demand better energy storage. Lithium-ion batteries have become a popular choice for meeting increased energy storage and power density needs. Like any energy solution, take for example the flammability of gasoline for automobiles, there are safety concerns surrounding the implications of failure. Although lithium-ion battery technology has existed for some time, the public interest in safety has become of higher concern with media stories reporting catastrophic cellular phone- and electric vehicle failures. Lithium-ion battery failure can be dangerously volatile. Because of this, battery electrochemical and thermal response is important to understand in order to improve safety when designing products that use lithium-ion chemistry. The implications of past and present understanding of multi-physics relationships inside a lithium-ion cell allow for the study of variables impacting cell response when designing new battery packs. Specifically, state-of-the-art design tools and models incorporate battery condition monitoring, charge balancing, safety checks, and thermal management by estimation of the state of charge, state of health, and internal electrochemical parameters. The parameters are well understood for healthy batteries and more recently for aging batteries, but not for physically damaged cells. Combining multi-physics and multi-scale modeling, a framework for isolating individual parameters to understand the impact of physical damage is developed in this work. The individual parameter isolated is the porosity of the separator, a critical component of the cell. This provides a powerful design tool for researchers and OEM engineers alike. This work is a partnership between a battery OEM (Johnson Controls, Inc.), a Computer Aided Engineering tool maker (ANSYS, Inc.), and a university laboratory (Advanced Manufacturing and Design Lab, University of Wisconsin-Milwaukee). This work aims at bridging the gap between industry and academia by using a computer aided engineering (CAE) platform to focus battery design for safety