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

An Intelligent System for Induction Motor Health Condition Monitoring

Induction motors (IMs) are commonly used in both industrial applications and household appliances. An IM online condition monitoring system is very useful to identify the IM fault at its initial stage, in order to prevent machinery malfunction, decreased productivity and even catastrophic failures. Although a series of research efforts have been conducted over decades for IM fault diagnosis using various approaches, it still remains a challenging task to accurately diagnose the IM fault due to the complex signal transmission path and environmental noise. The objective of this thesis is to develop a novel intelligent system for more reliable IM health condition monitoring. The developed intelligent monitor consists of two stages: feature extraction and decision-making. In feature extraction, a spectrum synch technique is proposed to extract representative features from collected stator current signals for fault detection in IM systems. The local bands related to IM health conditions are synchronized to enhance fault characteristic features; a central kurtosis method is suggested to extract representative information from the resulting spectrum and to formulate an index for fault diagnosis. In diagnostic pattern classification, an innovative selective boosting technique is proposed to effectively classify representative features into different IM health condition categories. On the other hand, IM health conditions can also be predicted by applying appropriate prognostic schemes. In system state forecasting, two forecasting techniques, a model-based pBoost predictor and a data-driven evolving fuzzy neural predictor, are proposed to forecast future states of the fault indices, which can be employed to further improve the accuracy of IM health condition monitoring. A novel fuzzy inference system is developed to integrate information from both the classifier and the predictor for IM health condition monitoring. The effectiveness of the proposed techniques and integrated monitor is verified through simulations and experimental tests corresponding to different IM states such as IMs with broken rotor bars and with the bearing outer race defect. The developed techniques, the selective boosting classifier, pBoost predictor and evolving fuzzy neural predictor, are effective tools that can be employed in a much wider range of applications. In order to select the most reliable technique in each processing module so as to provide a more positive assessment of IM health conditions, some more techniques are also proposed for each processing purpose. A conjugate Levebnerg-Marquardt method and a Laplace particle swarm technique are proposed for model parameter training, whereas a mutated particle filter technique is developed for system state prediction. These strong tools developed in this work could also be applied to fault diagnosis and other applications

De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics

The usage of Lithium-ion (Li-ion) batteries has gained widespread popularity across various industries, from powering portable electronic devices to propelling electric vehicles and supporting energy storage systems. A central challenge in Li-ion battery reliability lies in accurately predicting their Remaining Useful Life (RUL), which is a critical measure for proactive maintenance and predictive analytics. This study presents a novel approach that harnesses the power of multiple denoising modules, each trained to address specific types of noise commonly encountered in battery data. Specifically, a denoising auto-encoder and a wavelet denoiser are used to generate encoded/decomposed representations, which are subsequently processed through dedicated self-attention transformer encoders. After extensive experimentation on NASA and CALCE data, a broad spectrum of health indicator values are estimated under a set of diverse noise patterns. The reported error metrics on these data are on par with or better than the state-of-the-art reported in recent literature.Comment: 8 pages, 6 figures, 3 table

Modelling and estimation in lithium-ion batteries: a literature review

Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.This research received support from the Spanish Ministry of Science and Innovation under projects MAFALDA (PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033/ ERDF,EU) and MASHED (TED2021-129927B-I00), and by FI Joan Oró grant (code 2023 FI-1 00827), cofinanced by the European Union.Peer ReviewedPostprint (published version

Review of computational parameter estimation methods for electrochemical models

Electrochemical models are an incipient technique for estimation of battery cells internal variables, useful for cells design or state of function optimization. One of the non-trivial procedures that allow the use of this type of models is the estimation of model parameter values. This paper presents a review of the existing computational parameter estimation methods for rocking chair batteries electrochemical models, a crucial step for real case implementation. Physics-based models cannot reach accurate predictions if the parameters are not properly estimated, what highlights the necessity of reviewing the validity of these protocols, that are not extensively treated within literature. The gathered methods are explained and analyzed taking into account the accuracy and extent of the presented results, to give the most objective overview of their applicability within real case scenarios. The methods are classified into two different groups: single optimization analysis (using only one optimization procedure to estimate parameters) and multiple optimization analysis (methods using multiple optimizations). In addition, the need for at least some amount of physico-chemical characterization is analyzed as a common procedure for all the parameter estimation methods. The accuracy of each method is determined, taking as reference the best achievements found in literature. The results show that it is possible to estimate parameters with a high accuracy using non-invasive parameter estimation methods. Finally the potential of mixed (non invasive and physico-chemical based) methodologies is presented. These type of estimation procedures can potentially increase the accuracy of the procedures by lightening up the optimizations involved in the processes, and increasing the ability to estimate values for insensitive parameters. These mixed methods could achieve faster and cheaper estimation protocols, making them more efficient in general

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Advanced Battery Technologies: New Applications and Management Systems

In recent years, lithium-ion batteries (LIBs) have been increasingly contributing to the development of novel engineering systems with energy storage requirements. LIBs are playing an essential role in our society, as they are being used in a wide variety of applications, ranging from consumer electronics, electric mobility, renewable energy storage, biomedical applications, or aerospace systems. Despite the remarkable achievements and applicability of LIBs, there are several features within this technology that require further research and improvements. In this book, a collection of 10 original research papers addresses some of those key features, including: battery testing methodologies, state of charge and state of health monitoring, and system-level power electronics applications. One key aspect to emphasize when it comes to this book is the multidisciplinary nature of the selected papers. The presented research was developed at university departments, institutes and organizations of different disciplines, including Electrical Engineering, Control Engineering, Computer Science or Material Science, to name a few examples. The overall result is a book that represents a coherent collection of multidisciplinary works within the prominent field of LIBs