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

A Review: Prognostics and Health Management in Automotive and Aerospace

Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive- and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily.Algorithms and the Foundations of Software technolog

Data analysis and machine learning approaches for time series pre- and post- processing pipelines

157 p.En el ámbito industrial, las series temporales suelen generarse de forma continua mediante sensores quecaptan y supervisan constantemente el funcionamiento de las máquinas en tiempo real. Por ello, esimportante que los algoritmos de limpieza admitan un funcionamiento casi en tiempo real. Además, amedida que los datos evolución, la estrategia de limpieza debe cambiar de forma adaptativa eincremental, para evitar tener que empezar el proceso de limpieza desde cero cada vez.El objetivo de esta tesis es comprobar la posibilidad de aplicar flujos de aprendizaje automática a lasetapas de preprocesamiento de datos. Para ello, este trabajo propone métodos capaces de seleccionarestrategias óptimas de preprocesamiento que se entrenan utilizando los datos históricos disponibles,minimizando las funciones de perdida empíricas.En concreto, esta tesis estudia los procesos de compresión de series temporales, unión de variables,imputación de observaciones y generación de modelos subrogados. En cada uno de ellos se persigue laselección y combinación óptima de múltiples estrategias. Este enfoque se define en función de lascaracterísticas de los datos y de las propiedades y limitaciones del sistema definidas por el usuario

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.publishedVersio

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

Optimizing the Implementation of Green Technologies Under Climate Change Uncertainty

In this study, we aim to investigate the application of the green technologies (i.e., green roofs (GRs), Photovoltaic (PV) panels, and battery integrated PV systems) under climate change-related uncertainty through three separate, but inherently related studies, and utilize optimization methods to provide new solutions or improve the currently available methodsFirst, we develop a model to evaluate and optimize the joint placement of PV panels and GRs under climate change uncertainty. We consider the efficiency drop of PV panels due to heat, savings from GRs, and the interaction between them. We develop a two-stage stochastic programming model to optimally place PV panels and GRs under climate change uncertainty to maximize the overall profit. We calibrate the model and then conduct a case study on the City of Knoxville, TN.Second, we study the diffusion rate of the green technologies under different climate projections for the City of Knoxville through the integration of simulation and dynamic programming. We aim to investigate the diffusion rates for PV panels and/or GRs under climate change uncertainty in the City of Knoxville, TN. We further investigate the effect of different and evaluate their effects on the diffusion rate. We first present the agent based framework and the mathematical model behind it. Then, we study the effects of different policies on the results and rate of diffusion.Lastly, We aim to study a Lithium-ion battery load connected to a PV system to store the excess generated electricity throughout the day. The stored energy is then used when the PV system is not able to generate electricity due to a lack of direct solar radiation. This study is an attempt to minimize the cost of electricity bill for a medium sized household by maximizing the battery package utilization. We develop a Markov decision processes (MDP) model to capture the stochastic nature of the panels\u27 output due to weather. Due to the minute reduction in the Li-ion battery capacity per day, we have to deal with an excessively large state space. Hence, we utilize reinforcement learning methods (i.e., Q-Learning) to find the optimal policy