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

Optimal Model Reduction of Lithium-Ion Battery Systems Using Particle Swarm Optimization

Lithium-ion batteries (LIBs) have been widely used as an energy storage mechanism among all the types of rechargeable batteries owing to their high energy and power density. Because of the vast applications of LIBs in several dynamic operations, the development of a robust model to simulate the battery’s dynamic behavior and performance for control and system design is paramount. Several modeling efforts have been invested into the development of electrochemical models for simulation of LIB systems ranging from a full-order model, the so-called Doyle-Fuller-Newman (DFN) model to several reduced-order models. This thesis work involves the development of a reduced-order electrochemical model based on single particle approach with electrolyte dynamics (SPMe). The partial differential equations (PDEs) that capture the dynamic behavior and performance characteristics of the LIB systems were solved numerically through a finite difference method in MATLAB environment. For model reduction purpose, a constrained optimization problem was formulated to determine the optimal uneven discretization node points needed to numerically solve the battery PDEs for both solid and electrolyte phase concentration predictions. The optimization problem was solved using a particle swarm optimization (PSO) by minimizing the errors between the reference model, a SPMe with even discretization and the reduced model, a SPMe with uneven discretization. The proposed approach is similar to that proposed by Lee T.K. and Filipi Z., but differs because of the inclusion of electrolyte dynamics. The battery voltage was computed based on the optimal uneven discretization nodes under three different charging/discharging conditions. The proposed model demonstrates that as the number of optimal uneven discretization nodes applied to the model increases, the fidelity of the model increase. However, no significant improvement of prediction accuracy is observed after a certain level of uneven discretization. The proposed model demonstrates that in comparison to the evenly discretized model, the complexity in terms of the number of states can be reduced by 7 times without loss of physical interpretation of the diffusion and migration dynamics in the solid particles and electrolyte. This reduction in the number of discretization allows for faster computation for the purpose of control and system design.Master of Science in EngineeringEnergy Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/148848/1/Isaiah Oyewole Final Thesis Draft .pdfDescription of Isaiah Oyewole Final Thesis Draft .pdf : Thesi

Méthode inverse pour estimer les paramètres électrochimiques et thermophysiques des batteries aux ions lithium composées de différents matériaux pour l’électrode positive

La sécurité de plusieurs systèmes électriques est fortement dépendante de la fiabilité de leur bloc-batterie à base de piles aux ions lithium (Li-ion). Par conséquent, ces batteries doivent être suivis et contrôlés par un système de gestion des batteries (BMS). Le BMS interagit avec toutes les composantes du bloc-batterie de façon à maintenir leur intégrité. La principale composante d’un BMS est un modèle représentant le comportement des piles Liion et capable de prédire ses différents points d’opération. Dans les industries de l’électronique et de l’automobile, le BMS repose habituellement sur des modèles empiriques simples. Ceux-ci ne sont cependant pas capables de prédire les paramètres de la batterie lorsqu’elle vieillit. De plus, ils ne sont applicables que pour des piles spécifiques. D’un autre côté, les modèles électrochimiques sont plus sophistiqués et plus précis puisqu’ils sont basés sur la résolution des équations de transport et de cinétique électrochimique. Ils peuvent être utilisés pour simuler les caractéristiques et les réactions à l’intérieur des piles aux ions lithium. Pour résoudre les équations des modèles électrochimiques, il faut connaître les différents paramètres électrochimiques et thermo-physiques de la pile. Les variables les plus significatives des piles Li-ion peuvent être divisées en 3 catégories : les paramètres géométriques, ceux définissant les matériaux et les paramètres d’opération. Les paramètres géométriques et de matériaux peuvent être facilement obtenus à partir de mesures directes ou à partir des spécifications du manufacturier. Par contre, les paramètres d’opération ne sont pas faciles à identifier. De plus, certains d’entre eux peuvent dépendre de la technique de mesure utilisée et de l’âge. Finalement, la mesure de certains paramètres requiert le démantèlement de la pile, une procédure risquée et destructive. Plusieurs recherches ont été réalisées afin d’identifier les paramètres opérationnels des piles aux ions lithium. Toutefois, la plupart de ces études ont porté sur l’estimation d’un nombre limité de paramètres et se sont attardées sur un seul type de matériau pour l’électrode positive utilisé dans la fabrication des piles Li-ion. De plus, le couplage qui existe entre les paramètres électrochimiques et thermo-physiques est complètement ignoré. Le but principal de cette thèse est de développer une méthode générale pour identifier simultanément différents paramètres électrochimiques et thermo-physiques et de prédire la performance des piles Li-ion à base de différents matériaux d’électrodes positives. Pour atteindre ce but, une méthode inverse efficace a été introduite. Des modèles directs représentatifs des piles Li-ion à base de différents matériaux d’électrodes positives ont également été développés. Un modèle rapide et précis simulant la performance de piles Li-ion avec des électrodes positives à base de LiMn2O4 ou de LiCoO2 est présenté. Également, deux modèles ont été développés pour prédire la performance des piles Li-ion avec une électrode positive de LiFePO4. Le premier, appelé modèle mosaïque modifié (MM), est basé sur une approche macroscopique alors que le deuxième, appelé le modèle mésoscopique, est plutôt basé sur une approche microscopique. Des études d’estimation de paramètres ont été conduites en utilisant les modèles développés et des données expérimentales fournies par Hydro-Québec. Tous les paramètres électrochimiques et thermo-physiques des piles Li-ions ont été simultanément identifiés et appliqués à la prédiction de la performance des piles. Finalement, une technique en temps réel reposant sur des réseaux de neurones est introduite dans la méthode d’estimation des paramètres intrinsèques au piles Li-ion.Abstract : The safety of many electrical systems is strongly dependent on the reliable operation of their lithium-ion (Li-ion) battery packs. As a result, the battery packs must be monitored by a battery management system (BMS). The BMS interacts with all the components of the system so as to maintain the integrity of the batteries. The main part of a BMS is a Li-ion battery model that simulates and predicts its different operating points. In the electronics and in the automobile industries, the BMS usually rests on simple empirical models. They are however unable to predict the battery parameters as it ages. Furthermore, they are only applicable to a specific cell. Electrochemical-based models are, on the other hand, more sophisticated and more precise. These models are based on chemical/electrochemical kinetics and transport equations. They may be used to simulate the Li-ion battery characteristics and reactions. In order to run the electrochemical-based mathematical models, it is imperative to know the different electrochemical and thermophysical parameters of the battery. The significant variables of the Li-ion battery can be classified into three groups: geometric, material and operational parameters. The geometric and material parameters can be easily obtained from direct measurements or from the datasheets provided by the manufacturer. The operational properties are, on the other hand, not easily available. Furthermore, some of them may vary according to the measurement techniques or the battery age. Sometimes, the measurement of these parameters requires the dismantling of the battery itself, which is a risky and destructive procedure. Many investigations have been conducted to identify the operational parameters of Li-ion batteries. However, most of these studies focused on the estimation of limited parameters, or considered only one type of the positive electrode materials used in Li-ion batteries. Moreover, the coupling of the thermophysical parameters to the electrochemical variables is ignored in all of them. The main goal of this thesis is to develop a general method to simultaneously identify different electrochemical and thermophysical parameters and to predict the performance of Li-ion batteries with different positive electrode materials. To achieve this goal, an effective inverse method is introduced. Also, direct models representative of Li-ion batteries are developed, applicable for all of the positive electrode materials. A fast and accurate model is presented for simulating the performance of the Li-ion batteries with the LiMn2O4 and LiCoO2 positive electrodes. Moreover, two macro- and micro-based models are developed for predicting the performance of Li-ion battery with the LiFePO4 positive electrode, namely the Modified Mosaic (MM) and the mesoscopic-based models. The parameter estimation studies are then implemented by means of the developed direct models and experimental data provided by Hydro-Québec. All electrochemical and thermophysical parameters of the Li-ion batteries are simultaneously identified and applied for the prediction of the battery performance. Finally, a real-time technique resting on neural networks is used for the estimation of the Li-ion batteries intrinsic parameters