A review on electrical and mechanical performance parameters in lithium-ion battery packs

Lithium-ion batteries are the most prominent power source for electric vehicles. The continues use at different environmental conditions demand accurate electrical and mechanical functionality. Most of the research paper published provide information to describe these conditions covering only one or a very few parameters. It leaves aside a holistic and comprehensive study to evaluate performance in lithium-ion battery packs. This review paper presents more than ten performance parameters with experiments and theory undertaken to understand the influence on the performance, integrity, and safety in lithium-ion battery packs. However, when the parameters are reviewed, it is concluded, that vibration and temperature critically affect the electrical and mechanical performance and are inherent to the operation conditions. Through the present work, it was found that limited literature exist that clearly define the influence of temperature and vibration. Therefore, comprehensive research still needs to evaluate the influence of the thermo-mechanical coupled loads on the battery performance. This review concluded that it is fundamental to perform the mentioned research to improve the battery pack performance and safety. In addition, it proposes an innovative technical solution to the automotive industry and can be a novel contribution to academia

Stochastic optimal control of Lithium-Ion battery operations

El control òptim en bateries de ions de liti maximitza la vida útil de la bateria alhora que garanteix la càrrega ràpida i un ús segur. Les propietats de les bateries fabricades poden diferir del valor de disseny i canviar amb el temps en degradar-se. La limitació d'anteriors simulacions de bateries és que utilitzen valors deterministes per aquests paràmetres que no es coneixen amb precisió. L'anàlisi estocàstic inclou aquestes incerteses d'aplicacions reals en simulacions. Després d'integrar les incerteses, l'objectiu és quantificar-ne la propagació per veure com afecta als estats finals. Aquesta investigació integra incerteses estocàstiques en el control de bateries òptim definint amb distribucions de probabilitat alguns paràmetres, com la temperatura ambient. La propagació de la incertesa es fa utilitzant anàlisi de sensibilitat lineal. S'ha dissenyat una metodologia per quantificar aquesta propagació d'incertesa a les bateries de ions de liti per a qualsevol conjunt de paràmetres incerts i qualsevol model de càrrega òptim. Aquesta metodologia pot calcular sensibilitats lineals en qualsevol sistema d'equacions discretes i algebraiques. En simulació de bateries, permet calcular sensibilitats en sistemes de càrrega híbrids (continus-discrets) resolent els inconvenients típics d'implementar salts discontinus o controlar estats no observables. La metodologia dissenyada és una eina versàtil per a simulacions estocàstiques de bateries. Pot redefinir rutes de càrrega òptimes i precises per futures aplicacions en temps real o per determinar noves especificacions de fabricació més segures. Per demostrar la precisió d'aquest mètode, els resultats presenten múltiples casos d'estudi, incloent la cinètica de reacció i els models de càrrega òptims. Els exemples consideren els efectes de definir una temperatura ambient incerta en estats de la bateria com el voltatge, la temperatura o l'estat de salut per protocols rellevants de càrrega òptima.El control óptimo de baterías de iones de litio maximiza la vida útil de la batería garantizando también la carga rápida y un uso seguro. Las propiedades de las baterías fabricadas pueden diferir de los valores de diseño o cambiar con el tiempo por degradación. La limitación de anteriores simulaciones de baterías es que utilizan valores deterministas para parámetros que no se conocen con precisión. El análisis estocástico incluye estas incertidumbres reales en las simulaciones. Después de integrar las incertidumbres, el objetivo es cuantificar su propagación para saber cómo afectan a los estados finales. Esta investigación integra incertidumbres en el control óptimo de baterías definiendo como distribuciones de probabilidad algunos parámetros del modelo, como la temperatura ambiente. La propagación de la incertidumbre se implementa utilizando análisis de sensibilidad lineal. Se ha diseñado una metodología para cuantificar esta propagación de incertidumbre en baterías de iones de litio para cualquier conjunto de parámetros inciertos y modelo de carga óptima. Esta metodología permite calcular sensibilidades lineales en cualquier sistema de ecuaciones discretas y algebraicas. Para simulaciones de baterías, puede calcular sensibilidades en sistemas de carga híbridos (continuos-discretos) resolviendo limitaciones comunes en saltos discontinuos y controlar estados no observables. La metodología utilizada es una herramienta versátil para simular baterías estocásticas. Puede redefinir rutas de carga óptimas y precisas para futuras aplicaciones en tiempo real o para determinar nuevas especificaciones de fabricación más seguras. Para demostrar la precisión de este método, los resultados presentan múltiples casos de estudio, incluida la cinética de reacción y modelos de carga óptimos. Los ejemplos consideran la implicación de definir una temperatura ambiente incierta en estados de la batería como el voltaje o el estado de salud en protocolos relevantes de carga óptima.Optimal charging of lithium-ion batteries maximizes battery life while ensuring fast charging and safe usage. The properties of manufactured batteries can differ from design values and change over time due to degradation. The limitation of past battery simulations is that they use fixed deterministic values for these parameters that may not be accurately known. Stochastic analysis includes real-world uncertainties in simulations to represent this manufacturing variation. This study aims to propagate the uncertainty of model parameters onto output states, such as voltage or cell temperature. This research integrates stochastic uncertainties in optimal battery control by using probabilistic distributions to define model parameters such as the ambient temperature. The uncertainty propagation is then performed using linear sensitivity analysis. The linearized sensitivity is validated using Monte Carlo with several hundreds of replicates, proving that sensitivity analysis is significantly less computationally expensive. A methodology is designed to quantify uncertainty propagation in lithium-ion batteries for any set of probabilistic parameters and optimal charging paths. This methodology computes linear sensitivities on any system of differential-algebraic equations. For battery modeling, it can accurately compute sensitivities on mixed continuous-discrete simulations, solving typical issues found with discrete stages and the control of non-measurable states. The methodology given is a powerful tool for stochastic battery simulations. It can help redefine accurate optimal charging paths for future onboard applications and determine safer manufacturing specifications. Multiple case studies are presented to validate this methodology, including reaction kinetics and optimal charging paths. The examples analyzed consider how an uncertain ambient temperature affects battery's voltage, temperature, and state of health for relevant optimal charging protocols.Outgoin

A review of image-based simulation applications in high-value manufacturing

Image-Based Simulation (IBSim) is the process by which a digital representation of a real geometry is generated from image data for the purpose of performing a simulation with greater accuracy than with idealised Computer Aided Design (CAD) based simulations. Whilst IBSim originates in the biomedical field, the wider adoption of imaging for non-destructive testing and evaluation (NDT/NDE) within the High-Value Manufacturing (HVM) sector has allowed wider use of IBSim in recent years. IBSim is invaluable in scenarios where there exists a non-negligible variation between the ‘as designed’ and ‘as manufactured’ state of parts. It has also been used for characterisation of geometries too complex to accurately draw with CAD. IBSim simulations are unique to the geometry being imaged, therefore it is possible to perform part-specific virtual testing within batches of manufactured parts. This novel review presents the applications of IBSim within HVM, whereby HVM is the value provided by a manufactured part (or conversely the potential cost should the part fail) rather than the actual cost of manufacturing the part itself. Examples include fibre and aggregate composite materials, additive manufacturing, foams, and interface bonding such as welding. This review is divided into the following sections: Material Characterisation; Characterisation of Manufacturing Techniques; Impact of Deviations from Idealised Design Geometry on Product Design and Performance; Customisation and Personalisation of Products; IBSim in Biomimicry. Finally, conclusions are drawn, and observations made on future trends based on the current state of the literature

A GENERIC RELIABILITY ANALYSIS AND DESIGN FRAMEWORK WITH RANDOM PARAMETER, FIELD, AND PROCESS VARIABLES

This dissertation aims at developing a generic reliability analysis and design framework that enables reliability prediction and design improvement with random parameter, field, and process variables. The capability of this framework is further improved by predicting and managing reliability even with a dearth of data that can be used to characterize random variables. To accomplish the research goal, three research thrusts are set forth. First, advanced techniques are developed to characterize the random field or process. The fundamental idea of these techniques is to model the random field or process with a set of important field signatures and random variables. These techniques enable the use of random parameter, field, and process variables for reliability analysis and design even with a dearth of data. Second, a generic reliability analysis framework is proposed to accurately assess system reliability in the presence of random parameter, field, and process variables. An advanced probability analysis technique, the Eigenvector Dimension Reduction (EDR) method, is developed by integrating the Dimension Reduction (DR) method with three proposed improvements: 1) an eigenvector sampling approach to obtain statistically independent samples over a random space; 2) a Stepwise Moving Least Square (SMLS) method to accurately approximate system responses over a random space; and 3) a Probability Density Function (PDF) generation method to accurately approximate the PDF of system responses for reliability analysis. Third, a generic Reliability-Based Design Optimization (RBDO) framework is developed to solve engineering design problems with random parameter, field, and process variables. This design framework incorporates the EDR method into RBDO. To illustrate the effectiveness of the developed framework, many numerical and engineering examples are employed to conduct the reliability analysis and RBDO with random parameter, field, and process variables. This dissertation demonstrates that the developed framework is very accurate and efficient for the reliability analysis and RBDO of engineering products and processes

Biorefarmeries: Milking ethanol from algae for the mobility of tomorrow

The idea of this project is to fully exploit microalgae to the best of its potential, possibly proposing a sort of fourth generation fuel based on a continuous milking of macro- and microorganisms (as cows in a milk farm), which produce fuel by photosynthetic reactions. This project proposes a new transportation concept supported by a new socio-economic approach, in which biofuel production is based on biorefarmeries delivering fourth generation fuels which also have decarbonization capabilities, potential negative CO2 emissions plus positive impacts on mobility, the automotive Industry, health and environment and the econom

Hybrid Twin in Complex System Settings

Los beneficios de un conocimiento profundo de los procesos tecnológicos e industriales de nuestro mundo son incuestionables. La optimización, el análisis inverso o el control basado en la simulación son algunos de los procedimientos que pueden llevarse a cabo una vez que los conocimientos anteriores se transforman en valor para las empresas. Con ello se consiguen mejores tecnologías que acaban beneficiando enormemente a la sociedad. Pensemos en una actividad rutinaria para muchas personas hoy en día, como coger un avión. Todos los procedimientos anteriores se llevan a cabo en el diseño del avión, en el control a bordo y en el mantenimiento, lo que culmina en un producto tecnológicamente eficiente en cuanto a recursos. Este alto valor añadido es lo que está impulsando a la Ciencia de la Ingeniería Basada en la Simulación (Simulation Based Engineering Science, SBES) a introducir importantes mejoras en estos procedimientos, lo que ha supuesto avances importantes en una gran variedad de sectores como la sanidad, las telecomunicaciones o la ingeniería.Sin embargo, la SBES se enfrenta actualmente a varias dificultades para proporcionar resultados precisos en escenarios industriales complejos. Una de ellas es el elevado coste computacional asociado a muchos problemas industriales, que limita seriamente o incluso inhabilita los procesos clave descritos anteriormente. Otro problema es que, en otras aplicaciones, los modelos más precisos (que a su vez son los más caros computacionalmente) no son capaces de tener en cuenta todos los detalles que rigen el sistema físico estudiado, con desviaciones observadas que parecen escapar de nuestro conocimiento.Por lo tanto, en este contexto, a lo largo de este manuscrito se proponen novedosas estrategias y técnicas numéricas para hacer frente a los retos a los que se enfrenta la SBES. Para ello, se analizan diferentes aplicaciones industriales.El panorama anterior junto con el exhaustivo desarrollo producido en la Ciencia de Datos, brinda además una oportunidad perfecta para los denominados Dynamic Data Driven Application Systems (DDDAS), cuyo objetivo principal es fusionar los algoritmos clásicos de simulación con los datos procedentes de medidas experimentales. En este escenario, los datos y las simulaciones ya no estarían desacoplados, sino que formarían una relación simbiótica que alcanzaría hitos inconcebibles hasta estos días. Más en detalle, los datos ya no se entenderán como una calibración estática de un determinado modelo constitutivo, sino que el modelo se corregirá dinámicamente tan pronto como los datos experimentales y las simulaciones tiendan a diverger.Por esta razón, la presente tesis ha hecho especial énfasis en las técnicas de reducción de modelos, ya que no sólo son una herramienta para reducir la complejidad computacional, sino también un elemento clave para cumplir con las restricciones de tiempo real que surgen del marco de los DDDAS.Además, esta tesis presenta nuevas metodologías basadas en datos para enriquecer el denominado paradigma Hybrid Twin. Un paradigma cuya motivación radica en su habilidad de posibilitar los DDDAS. ¿Cómo? combinando soluciones paramétricas y técnicas de reducción de modelos con correcciones dinámicas generadas “al vuelo'' basadas en los datos experimentales recogidos en cada instante.<br /

NASA Tech Briefs, June 2001

Topics covered include: Sensors; Electronic Components and Systems; Software Engineering; Materials; Manufacturing/Fabrication; physical Sciences; Information Sciences

A systematic approach for developments of gas species modelling of a Li-ion battery cell induced by thermal runaway event

The ever-increasing demands in Lithium-ion battery (LIBs) technology have become the pinnacle for the energy storage industry. LIBs cover wide range of application, from small portable devices such as mobile phones and laptops, to larger applications e.g. power grids and transport. To meet these energy demands, research has focused much resource on developing cell technologies that can fulfil the requirements. As a result, today’s LIB technology has much higher magnitudes of energy and power density, in comparison to those first introduced back in 1991 to power a video camera. However, there are associated consequences that come with these increased attributes. That each cell can now store higher energy, this in turn can result in the LIB failure mode becoming more severe in effect. The worst-case scenario is a condition in which a cell rapidly ignites – an event known as a thermal runaway (TR). This topic has attracted profound attention in battery safety research. Research into battery safety has always been one of the prime topics when developing new LIBs. A considerable amount of research has been previously performed experimentally and by simulation work. However, there is a significant gap identified in the simulation domain, as there is insufficient research on gas species modelling at cell level during thermal runaway. This is especially so in the early phases of the reaction sequence. This thesis proposes a systematic approach method development around gas species modelling. Based on the literature review conducted, the development methods require the establishment of a basic electrochemical model, in which sensitive parameters are identified through characterisation methods such as SEM/EDS, XRD, particle analysis, and STA/FTIR. For model development, P2D electrochemical and thermal abuse modelling was used as a basis to be incorporated with the gas species model. The gas model was developed based on a neural-network (NN) learning approach. The result from cell characterization reveals that the material used in the cell was the same as some research which utilized the same cell material. For gas analysis results, an interesting finding was observed, which is the time for the cell to reach onset temperature after it was gassing are consistent regardless the cell SOC. Also reported in this thesis is the species of gas detected during the venting stages. In addition to these findings, the same observation from literature results were also apparent. This includes the effect of cell SOC towards maximum temperature during TR event and the shift of onset temperature to lower temperature as the SOC increase

Determining Material Structures and Surface Chemistry by Genetic Algorithms and Quantum Chemical Simulations

With the advent of modern computing, the use of simulation in chemistry has become just as important as experiment. Simulations were originally only applicable to small molecules, but modern techniques, such as density functional theory (DFT) allow extension to materials science. While there are many valuable techniques for synthesis and characterization in chemistry laboratories, there are far more materials possible than can be synthesized, each with an entire host of surfaces. This wealth of chemical space to explore begs the use of computational chemistry to mimic synthesis and experimental characterization. In this work, genetic algorithms (GA), for the former, and DFT calculations, for the latter, are developed and used for the in silico exploration of materials chemistry. Genetic algorithms were first theorized in 1975 by John Holland and over the years subsequently expanded and developed for a variety of purposes. The first application to chemistry came in the early 1990’s and surface chemistry, specifically, appeared soon after. To complement the ability of a GA to explore chemical space is a second algorithmic technique: machine learning (ML) wherein a program is able to categorize or predict properties of an input after reviewing many, many examples of similar inputs. ML has more nebulous origins than GA, but applications to chemistry also appeared in the 1990’s. A history perspective and assessment of these techniques towards surface chemistry follows in this work. A GA designed to find the crystal structure of layered chemical materials given the material’s X-ray diffraction pattern is then developed. The approach reduces crystals into layers of atoms that are transformed and stacked until they repeat. In this manner, an entire crystal need only be represented by its base layer (or two, in some cases) and a set of instructions on how the layers are to be arranged and stacked. Molecules that may be present may not quite behave in this fashion, and so a second set of descriptors exist to determine the molecule’s position and orientation. Finally, the lattice of the unit cell is specified, and the structure is built to match. The GA determines the structure’s X-ray diffraction pattern, compares it against a provided experimental pattern, and assigns it a fitness value, where a higher value indicates a better match and a more fit individual. The most fit individuals mate, exchanging genetic material (which may mutate) to produce offspring which are further subjected to the same procedure. This GA can find the structure of bulk, layered, organic, and inorganic materials. Once a material’s bulk structure has been determined, surfaces of the material can be derived and analyzed by DFT. In this thesis, DFT is used to validate results from the GA regarding lithium-aluminum layered double hydroxide. Surface chemistry is more directly explored in the prediction of adsorbates on surfaces of lithiated nickel-manganese-cobalt oxide, a common cathode material in lithium-ion batteries. Surfaces are evaluated at the DFT+U level of theory, which reduces electron over-delocalization, and the energies of the surfaces both bare and with adsorbates are compared. By applying first-principles thermodynamics to predict system energies under varying temperatures and pressures, the behavior of these surfaces in experimental conditions is predicted to be mostly pristine and bare of adsorbates. For breadth, this thesis also presents an investigation of the electronic and optical properties of organic semiconductors via DFT and time-dependent DFT calculations