A critical review of online battery remaining useful lifetime prediction methods.

Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

State Estimation of Li-ion Batteries Using Machine Learning Algorithms

Lithium-ion batteries are mainly utilized in electric vehicles, electric ships, etc. due to their virtue of high energy density, low self-discharge, and low costs. Electric vehicles are prone to accelerated battery degradation due to the high charging/discharging cycles and high peak power demand. Hence, efficient management of the batteries is a dire need in this regard. Battery management systems (BMS) have been developing to control, monitor, and measure the variables of the battery such as voltage, current, and temperature, to estimate the states of charge (SOC) and state of health (SOH) of the battery. This study is divided into three parts; in the first part, the SOC of the battery is estimated utilizing electrochemical impedance spectroscopy (EIS) measurements. The EIS measurements are obtained at different SOC and temperature levels. The highly correlated measurements with the SOC are then extracted to be used as input features. Gaussian process regression (GPR) and linear regression (LR) are employed to estimate the SOC of the battery. In the second part of this study, the EIS measurements at different SOC and temperature levels are employed to estimate the SOH of the battery. In this part, transfer learning (TL) along with deep neural network (DNN) is adopted to estimate the SOH of the battery at another outrange temperature level. The effect of the number of fixed layers is also investigated to compare the performance of various DNN models. The results indicate that the DNN with no fixed layer outclasses the other DNN model with one or more fixed layers. In the third part of this dissertation, the co-estimation of SOC and SOH is conducted as SOC and SOH are intertwined characteristics of the battery, and a change in one affects the other variation. First, the SOH of the battery is estimated using EIS measurements by GPR and DNN. The estimated SOH, along with online-measurable variables of the battery, i.e., voltage and current, are then utilized as input features for long-short term memory (LSTM) and DNN algorithms to estimate the SOC of the battery

Contribution au pronostic d'une pile à combustible de type PEMFC - approche par filtrage particulaire.

The development of new energy converters, more efficient and environment friendly, such as fuelcells, tends to accelerate. Nevertheless, their large scale diffusion supposes some guaranties in termsof safety and availability. A possible solution to do so is to develop Prognostics and HealthManagement (PHM) on these systems, in order to monitor and anticipate the failures, and torecommend the necessary actions to extend their lifetime. In this spirit, this thesis deals with theproposal of a prognostics approach based on particle filtering dedicated to PEMFCs.The reasoning focuses first on setting a formalization of the working framework and theexpectations. This is pursued by the development of a physic-based modelling enabling a state ofhealth estimation and its evolution in time. The state estimation is made thanks to particle filtering.Different variants of filters are considered on the basis of the literature and new proposals adaptedto PHM are proposed and compared to existing ones. State of health estimates given by the filter areused to predict the future state of the system and its remaining useful life. All the proposals arevalidated on four datasets from PEMFC following different mission profiles. The results show goodperformances for predictions and remaining useful life estimates before failure.Le développement de nouveaux convertisseurs d’énergie, plus efficients et plus respectueux del’environnement, tels que les piles à combustibles, tend à s’accélérer. Leur diffusion à grande échellesuppose cependant des garanties en termes de sécurité et de disponibilité. Une solution possiblepour ce faire est de développer des solutions de Prognostics and Health Management (PHM) de cessystèmes, afin de mieux les surveiller, anticiper les défaillances et recommander les actionsnécessaires à l’allongement de leur durée de vie. Dans cet esprit, cette thèse porte sur la propositiond’une approche de pronostic dédiée aux piles à combustibles de types PEMFC à l’aide de filtrageparticulaire.Le raisonnement s’attache tout d’abord à mettre en place une formalisation du cadre de travailainsi que des exigences de mise en. Ceci se poursuit par le développement d’un modèle basé sur laphysique permettant une estimation d’état de santé et de son évolution temporelle. L’estimationd’état est réalisée grâce à du filtrage particulaire. Différentes variantes de filtres sont considérées surla base d’une de la littérature et de nouvelles propositions adaptées au PHM sont formulées etcomparées à celles existantes. Les estimations d’état de santé fournies par le processus de filtragesont utilisées pour réaliser des prédictions de l’état de santé futur du système, puis de sa durée devie résiduelle. L’ensemble des propositions est validé sur 4 jeux de données obtenus sur des PEMFCsuivant des profils de mission variés. Les résultats montrent de bonnes performances deprédictions et d’estimations de durée de vie résiduelle avant défaillance

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described