Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

The potential of acoustic signatures to be used for State-of-Charge (SoC) estimation is demonstrated using artificial neural network regression models. This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual, and often arbitrary, waveform peak selection. For applications where computational economy is prioritised, simple metrics of statistical significance are used to formally identify the most informative waveform features. These alone can be exploited for SoC inference. It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value, challenging the more common peak selection methods which focus on the latter. Although later echoes represent greater through-thickness coverage, and are intuitively more information-rich, their presence is not guaranteed. Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states. It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly, while also improving the estimation accuracy. Most importantly, it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates, without any complementary voltage information. This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed. Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell. Mean estimation errors as low as 0.75% are achieved on a SoC scale of 0–100%

Parameter estimation and modeling of lithium and lithium-ion batteries

Specific characteristics of Li-ion batteries (LIBs) make them promising candidates for energy storage systems when compared with the others. High working voltage and energy density as well as green technology of LIBs are the reasons for increasing interest to use these electrochemical systems as the substitute of conventional combustion engine of automobiles. Consequently, the interest to study these technologies has increased recently and several models have been introduced to simulate their behavior. However, it is difficult to model all multiphysics phenomena happening inside such rechargeable batteries. Some important choices need to be made when one wants to select an appropriate model for considering the main physics elements and yet be simple enough for large time scale studies. Combining chemical/electrochemical kinetics and transport phenomena, electrochemical models have been introduced to tackle most important principles inside the cell. These models, however, require known electrochemical parameters which most of the time are hard to get experimentally. Parameter estimation (PE) techniques simplify extracting these representative parameters of the cell behaviour. In this study, a PE methodology has been introduced to estimate the most influential electrochemical parameters of LIBs considering different positive electrode materials. The methodology starts with simplifying the well-known pseudo-two-dimensional (P2D) model, the most complex and the most popular electrochemical engineering models for simulating porous electrodes and introducing an enhanced single particle model (SPM). Neglecting the micro-structure of LIB, major electrochemical parameters are detected at the cell level. Next, the best time domains and discharge current rates to estimate each parameter are estimated by virtue of sensitivity analyses. Owing to the fact that the behavior of LIBs depends on the active materials employed in the electrode, the proposed methodology is verified for three different positive electrode active materials including LiCoO2, LiMn2O4 and LiFePO4. Furthermore, focusing on LiFePO4 (LFP), as the most promising positive electrode active material, a new modification is proposed to the model to address special features of this material. In this regard, a simplified electrochemical model is equipped with a variable resistance equation whose coefficients are estimated by means of PE.Résumé : Les batteries au Li-ion (BLI) figurent parmi les technologies les plus prometteuses pour le design de systèmes de stockage d’énergie à cause de leurs caractéristiques intrinsèques. Leur grand voltage de travail, leur grande densité énergétique et leur impact écologique positif expliquent l’intérêt soutenu de l’utilisation des BLI pour remplacer par exemple les moteurs à explosion dans les applications de transport terrestre. Il n’est donc pas surprenant de constater que ces technologies ont eu une attention scientifique importante et que plusieurs auteurs ont développé des modèles numériques simulant leur comportement. Il reste cependant difficile de représenter tous les phénomènes multiphysiques qui se déroulent à l’intérieur des batteries rechargeables par des modèles mathématiques. Des compromis importants doivent être faits lorsqu’on doit choisir un modèle représentant les principaux phénomènes physico-chimiques tout en restant assez simple pour pouvoir l’utiliser dans des études s’échelonnant sur de larges périodes temps. Représentant à la fois la cinétique électrochimique et le transport de masse, les modèles électrochimiques ont été introduits pour prendre en compte les phénomènes les plus importants. Ces modèles demandent cependant de connaître tous les paramètres électrochimiques, des données qui sont difficiles à obtenir expérimentalement. Les techniques d’estimation de paramètres simplifient l’obtention de ces données critiques pour représenter le comportement de la pile. Dans cette étude, une méthode d’estimation de paramètres a été introduite pour estimer les paramètres électrochimiques des BLI les plus influents, en prenant en compte différents matériaux d’électrode positive. La méthode proposée repose sur une amélioration du modèle à particule unique, qui représente lui-même une simplification du modèle pseudo-2D, le modèle électrochimique le plus connu et le plus complexe dans le domaine de la simulation de piles à électrodes poreuses. Les paramètres électrochimiques les plus importants ont été identifiés en négligeant la micro-structure de la batterie au Li-ion. Une étude de sensibilité a ensuite permis d’identifier les domaines temporels et les courants de décharge les plus favorables pour l’identification de chaque paramètre. Étant donné que le comportement des BLI dépend fortement des matériaux actifs utilisés pour la fabrication des électrodes, la méthodologie proposée a été testée sur 3 matériaux actifs différents (LiCoO2, LiMn2O4 and LiFePO4) employés dans la fabrication industrielle d’électrodes positives. Finalement, une autre amélioration du modèle à particule unique a été proposée et testée afin de mieux représenter le comportement spécifique du LiFePO4 (LFP), un matériau actif parmi les plus prometteurs pour l’électrode positive. Plus précisément, un modèle électrochimique simplifié incluant une équation représentant la variation de résistance en fonction du degré de décharge a été développé et les coefficients de cette équation ont été déterminés au moyen de la méthode d’estimation de paramètres proposée

Thermal Characteristics and Safety Aspects of Lithium-Ion Batteries: An In-Depth Review

This paper provides an overview of the significance of precise thermal analysis in the context of lithium-ion battery systems. It underscores the requirement for additional research to create efficient methodologies for modeling and controlling thermal properties, with the ultimate goal of enhancing both the safety and performance of Li-ion batteries. The interaction between temperature regulation and lithium-ion batteries is pivotal due to the intrinsic heat generation within these energy storage systems. A profound understanding of the thermal behaviors exhibited by lithium-ion batteries, along with the implementation of advanced temperature control strategies for battery packs, remains a critical pursuit. Utilizing tailored models to dissect the thermal dynamics of lithium-ion batteries significantly enhances our comprehension of their thermal management across a wide range of operational scenarios. This comprehensive review systematically explores diverse research endeavors that employ simulations and models to unravel intricate thermal characteristics, behavioral nuances, and potential runaway incidents associated with lithium-ion batteries. The primary objective of this review is to underscore the effectiveness of employed characterization methodologies and emphasize the pivotal roles that key parameters—specifically, current rate and temperature—play in shaping thermal dynamics. Notably, the enhancement of thermal design systems is often more feasible than direct alterations to the lithium-ion battery designs themselves. As a result, this thermal review primarily focuses on the realm of thermal systems. The synthesized insights offer a panoramic overview of research findings, with a deeper understanding requiring consultation of specific published studies and their corresponding modeling endeavors

Modeling of Lithium-ion Battery Considering Temperature and Aging Uncertainties

This dissertation provides a systematic methodology for analyzing and solving the temperature and aging uncertainties in Li-ion battery modeling and states estimation in the electric vehicle applications. This topic is motivated by the needs of enhancing the performance and adaptability of battery management systems. In particular, temperature and aging are the most crucial factors that influence battery performance, modeling, and control. First, the basic theoretical knowledge of Li-ion battery modeling and State of Charge (SoC) estimation are introduced. The thesis presents an equivalent circuit battery model based SoC estimation using Adaptive Extended Kalman Filter (AEKF) algorithm to solve the initial SoC problem and provide good estimation result. Second, the thesis focuses on the understanding of the temperature-dependent performance of Li-ion battery. The temperature influence is investigated through Electrochemical Impedance Spectroscopy (EIS) tests to enhance the theoretical basis understanding and to derive model compensation functions for better model adaptability at different temperatures. Third, the battery aging mechanisms are revisited first and then a series of aging tests are conducted to understand the degradation path of Lithium-ion battery. Moreover, the incremental capacity analysis (ICA) based State of Health (SoH) estimation method xiv are applied to track battery aging level and develop the bias correction modeling method for aged battery. In the final phase, the study of parallel-connected battery packs is presented. The inconsistency problem due to different battery aging levels and its influence to parallel-connected packs are discussed. Based on simulation and experimental test results, it shows that the current difference in parallel connected cells is increased significantly at low SoC, despite the battery aging levels and the number of cells in parallel. In total, this dissertation utilizes physics-based battery modeling and states estimation method to optimize battery management under temperature and aging uncertainties in electric vehicle applications. The unique contributions include developing analytical compensation functions to improve equivalent circuit battery model adaptability under temperature uncertainty and developing ICA based SoH estimation and battery modeling method to overcome aging uncertainty.Ph.D.CECS Automotive Systems EngineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/134041/1/Gong Dissertation Final.pdfDescription of Gong Dissertation Final.pdf : Dissertatio

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

Multiscale Experimental Approaches to Li-ion Battery Research: From Particle Analysis to Optimized Battery Design.

We approach the challenges in Li-ion battery research through multiscale experiments: a small but macro scale Li-ion battery was designed for an implantable surgical device for distraction osteogenesis, while in particle- to micro-scale, the baseline cathode materials for Li-ion batteries were investigated for their structural and electrochemical characteristics. For the optimized battery design study, we first identified the power / energy requirements for a common clinical protocol using a novel distraction device developed in parallel to its battery design, and then ran an algorithm to select a commercially available battery with minimal volume that satisfied the system demands. A polymer Li-ion battery was selected due to high power and energy densities as well as its favorable geometry. A bench-top prototype device, integrating an actuator, a control circuit, and a battery, was fabricated to test its functionality and reliability, and eventually will be ready for animal implantation studies. Particle- to micro- scale experimental studies of Li-ion insertion metal oxide cathode materials were conducted using simple forms of the baseline materials, such as thin films and dispersed single particles, aiming to understand their structural characteristics and electrochemical properties. Various characterization techniques including SEM, TEM, XRD, and AFM were used to observe external and internal microscopic morphology of primary particles from candidate cathode materials for EV applications, such as LiFePO4, Li[Ni1/3Co1/3Mn1/3]O2, and LiMn2O4. Their anisotropic and inhomogeneous nature was revealed due to the hierarchic structure consisting of crystal grains and grain boundaries. Thin film study of LiMn2O4 also showed similarly complex microstructures that were found to be determined by their fabrication conditions, including substrate material and annealing temperature. In an experimental study with single LiMn2O4 particles, we take one step toward precise modeling and control of large format cells in EV applications by generating and incorporating accurate model parameters, including diffusion coefficients from CV and PITT methods, and realistic particle geometries from AFM scanning data. Simulation of Li-ion intercalation with the implemented experimental measurements showed that LiMn2O4 particles could be under higher intercalation-induced stress due to slower diffusion and local stress concentration at the grain boundaries.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64736/1/mdchung_1.pd

Experimental and computational study of self-heating ignition and calorimetry of Lithium-ion batteries during storage

Fire accidents involving Lithium-ion batteries (LIBs) threaten the safety of their storage facilities, where there are thousands of open-circuit cells stacked together forming ensembles. Ignition of ensembles could be triggered by self-heating but this important ignition phenomenon has received little attention in the literature. A few studies have investigated the self-heating of a single cell, but do not account for the effect of heat transfer. However, a large-size open-circuit LIB ensemble during storage can develop temperature gradients and therefore ignition is affected by both heat transfer and chemistry. In this thesis, I conducted ignition and calorimetry experiments using a commercial type of prismatic LiCoO2 cell to quantify self-heating conditions and find the chemical kinetics and thermal properties. Results show that self-heating ignition is possible when cells are stacked together and that the critical ambient temperature decreases with the number of cells. A computational model, based on open-source code Gpyro, is used to understand and predict ignition in different ensemble sizes and storage conditions. I used both ignition experiments and model predictions to quantify and compare two critical temperatures: the cell thermal runaway temperature defined in standard SAE-J2464, and the critical ambient temperature triggering ignition. I find that the cell thermal runaway temperature is insensitive to size, but the critical ambient temperature decreases with size. This shows that the critical ambient temperature should be used to design safe storage rather than the SAE standard. I further use the experiments and computational model to predict LIB ignition during storage with different states of charge and cathode materials. In order to understand whether the accelerating rate calorimetry (ARC) can properly quantify self-heating ignition, for the first time, I quantify the uncertainty caused by ignoring heat transfer in experiments. ARC can generally measure the onset of self-heating, but underestimate the heat of reaction, the maximum temperature and cannot measure critical ignition temperature. The results in this thesis help improve the safety of the open-circuit LIB storage and provide a scientific understanding of self-heating hazards and guidance for better standards.Open Acces

Driving behavior-guided battery health monitoring for electric vehicles using machine learning

An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe and reliable operation of electric vehicles (EVs). Feature-based machine learning methods have exhibited enormous potential for rapidly and precisely monitoring battery health status. However, simultaneously using various health indicators (HIs) may weaken estimation performance due to feature redundancy. Furthermore, ignoring real-world driving behaviors can lead to inaccurate estimation results as some features are rarely accessible in practical scenarios. To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions. We first summarized and analyzed various individual HIs with mechanism-related interpretations, which provide insightful guidance on how these features relate to battery degradation modes. Moreover, all features were carefully evaluated and screened based on estimation accuracy and correlation analysis on three public battery degradation datasets. Finally, the scenario-based feature fusion and acquisition probability-based practicality evaluation method construct a useful tool for feature extraction with consideration of driving behaviors. This work highlights the importance of balancing the performance and practicality of HIs during the development of feature-based battery health monitoring algorithms