Real-time Energy Management System of Battery-Supercapacitor in Electric vehicles

This thesis presents the design, simulation and experimental validation of an Energy Management System (EMS) for a Hybrid Energy Storage System (HESS) composed of lithium ion batteries and Supercapacitors (SCs) in electric vehicles. The aim of the EMS is to split the power demand considering the weaknesses and strengths or the power sources. The HESS requires an EMS to determine power missions for the battery and SC in real time, where the SC is commanded to assist the battery during high power demand and recover the energy generated during braking. Frequency sharing techniques have been proposed by researchers to achieve this objective, including the Discrete Wavelet Transform (DWT) and conventional filtration methods (low and high pass filters). However, filtration approaches can introduce delay (milliseconds to tens of seconds) in the frequency components which undermines the hybridisation advantages. Hence, the selection of the filtration technique and filter design are crucial to the system's performance. Researchers have proposed power demand prediction methodologies to deal with time delay, however, the advantages and drawbacks of using such methods have not been investigated thoroughly, particularly whether time delay compensation and its inherent prediction error improves the system performance, efficiency, and timely SC contribution during the motoring and braking stages. This work presents a fresh perspective to this research field by introducing a novel approach that deals with delay without complicated prediction algorithms and improves the SC contribution during the motoring and braking stages while reducing energy losses in the system. The proposed EMS allows the SC to provide timely assistance during motoring and to recover the braking energy generated. A charging strategy controls energy circulation between the battery and SC to keep the SC charge availability during the whole battery discharge cycle. The performance and efficiency of the HESS is improved when compared to the traditional use of conventional filtration techniques and the DWT. Results show that the proposed EMS method improves the energy efficiency of the HESS. For the US06 driving cycle, the energy efficiency is 91.6%. This is superior to the efficiency obtained with an EMS based on a high pass filter (41.3%), an EMS based on DWT high frequency component (30.3%) and an EMS based on the predicted DWT high frequency component (41%)

Exploring the adoption of a conceptual data analytics framework for subsurface energy production systems: a study of predictive maintenance, multi-phase flow estimation, and production optimization

Als die Technologie weiter fortschreitet und immer stärker in der Öl- und Gasindustrie integriert wird, steht eine enorme Menge an Daten in verschiedenen Wissenschaftsdisziplinen zur Verfügung, die neue Möglichkeiten bieten, informationsreiche und handlungsorientierte Informationen zu gewinnen. Die Konvergenz der digitalen Transformation mit der Physik des Flüssigkeitsflusses durch poröse Medien und Pipeline hat die Entwicklung und Anwendung von maschinellem Lernen (ML) vorangetrieben, um weiteren Mehrwert aus diesen Daten zu gewinnen. Als Folge hat sich die digitale Transformation und ihre zugehörigen maschinellen Lernanwendungen zu einem neuen Forschungsgebiet entwickelt. Die Transformation von Brownfields in digitale Ölfelder kann bei der Energieproduktion helfen, indem verschiedene Ziele erreicht werden, einschließlich erhöhter betrieblicher Effizienz, Produktionsoptimierung, Zusammenarbeit, Datenintegration, Entscheidungsunterstützung und Workflow-Automatisierung. Diese Arbeit zielt darauf ab, ein Rahmenwerk für diese Anwendungen zu präsentieren, insbesondere durch die Implementierung virtueller Sensoren, Vorhersageanalytik mithilfe von Vorhersagewartung für die Produktionshydraulik-Systeme (mit dem Schwerpunkt auf elektrischen Unterwasserpumpen) und präskriptiven Analytik für die Produktionsoptimierung in Dampf- und Wasserflutprojekten. In Bezug auf virtuelle Messungen ist eine genaue Schätzung von Mehrphasenströmen für die Überwachung und Verbesserung von Produktionsprozessen entscheidend. Diese Studie präsentiert einen datengetriebenen Ansatz zur Berechnung von Mehrphasenströmen mithilfe von Sensormessungen in elektrischen untergetauchten Pumpbrunnen. Es wird eine ausführliche exploratorische Datenanalyse durchgeführt, einschließlich einer Ein Variablen Studie der Zielausgänge (Flüssigkeitsrate und Wasseranteil), einer Mehrvariablen-Studie der Beziehungen zwischen Eingaben und Ausgaben sowie einer Datengruppierung basierend auf Hauptkomponentenprojektionen und Clusteralgorithmen. Feature Priorisierungsexperimente werden durchgeführt, um die einflussreichsten Parameter in der Vorhersage von Fließraten zu identifizieren. Die Modellvergleich erfolgt anhand des mittleren absoluten Fehlers, des mittleren quadratischen Fehlers und des Bestimmtheitskoeffizienten. Die Ergebnisse zeigen, dass die CNN-LSTM-Netzwerkarchitektur besonders effektiv bei der Zeitreihenanalyse von ESP-Sensordaten ist, da die 1D-CNN-Schichten automatisch Merkmale extrahieren und informative Darstellungen von Zeitreihendaten erzeugen können. Anschließend wird in dieser Studie eine Methodik zur Umsetzung von Vorhersagewartungen für künstliche Hebesysteme, insbesondere bei der Wartung von Elektrischen Untergetauchten Pumpen (ESP), vorgestellt. Conventional maintenance practices for ESPs require extensive resources and manpower, and are often initiated through reactive monitoring of multivariate sensor data. Um dieses Problem zu lösen, wird die Verwendung von Hauptkomponentenanalyse (PCA) und Extreme Gradient Boosting Trees (XGBoost) zur Analyse von Echtzeitsensordaten und Vorhersage möglicher Ausfälle in ESPs eingesetzt. PCA wird als unsupervised technique eingesetzt und sein Ausgang wird weiter vom XGBoost-Modell für die Vorhersage des Systemstatus verarbeitet. Das resultierende Vorhersagemodell hat gezeigt, dass es Signale von möglichen Ausfällen bis zu sieben Tagen im Voraus bereitstellen kann, mit einer F1-Bewertung größer als 0,71 im Testset. Diese Studie integriert auch Model-Free Reinforcement Learning (RL) Algorithmen zur Unterstützung bei Entscheidungen im Rahmen der Produktionsoptimierung. Die Aufgabe, die optimalen Injektionsstrategien zu bestimmen, stellt Herausforderungen aufgrund der Komplexität der zugrundeliegenden Dynamik, einschließlich nichtlinearer Formulierung, zeitlicher Variationen und Reservoirstrukturheterogenität. Um diese Herausforderungen zu bewältigen, wurde das Problem als Markov-Entscheidungsprozess reformuliert und RL-Algorithmen wurden eingesetzt, um Handlungen zu bestimmen, die die Produktion optimieren. Die Ergebnisse zeigen, dass der RL-Agent in der Lage war, den Netto-Barwert (NPV) durch kontinuierliche Interaktion mit der Umgebung und iterative Verfeinerung des dynamischen Prozesses über mehrere Episoden signifikant zu verbessern. Dies zeigt das Potenzial von RL-Algorithmen, effektive und effiziente Lösungen für komplexe Optimierungsprobleme im Produktionsbereich zu bieten.As technology continues to advance and become more integrated in the oil and gas industry, a vast amount of data is now prevalent across various scientific disciplines, providing new opportunities to gain insightful and actionable information. The convergence of digital transformation with the physics of fluid flow through porous media and pipelines has driven the advancement and application of machine learning (ML) techniques to extract further value from this data. As a result, digital transformation and its associated machine-learning applications have become a new area of scientific investigation. The transformation of brownfields into digital oilfields can aid in energy production by accomplishing various objectives, including increased operational efficiency, production optimization, collaboration, data integration, decision support, and workflow automation. This work aims to present a framework of these applications, specifically through the implementation of virtual sensing, predictive analytics using predictive maintenance on production hydraulic systems (with a focus on electrical submersible pumps), and prescriptive analytics for production optimization in steam and waterflooding projects. In terms of virtual sensing, the accurate estimation of multi-phase flow rates is crucial for monitoring and improving production processes. This study presents a data-driven approach for calculating multi-phase flow rates using sensor measurements located in electrical submersible pumped wells. An exhaustive exploratory data analysis is conducted, including a univariate study of the target outputs (liquid rate and water cut), a multivariate study of the relationships between inputs and outputs, and data grouping based on principal component projections and clustering algorithms. Feature prioritization experiments are performed to identify the most influential parameters in the prediction of flow rates. Model comparison is done using the mean absolute error, mean squared error and coefficient of determination. The results indicate that the CNN-LSTM network architecture is particularly effective in time series analysis for ESP sensor data, as the 1D-CNN layers are capable of extracting features and generating informative representations of time series data automatically. Subsequently, the study presented herein a methodology for implementing predictive maintenance on artificial lift systems, specifically regarding the maintenance of Electrical Submersible Pumps (ESPs). Conventional maintenance practices for ESPs require extensive resources and manpower and are often initiated through reactive monitoring of multivariate sensor data. To address this issue, the study employs the use of principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to analyze real-time sensor data and predict potential failures in ESPs. PCA is utilized as an unsupervised technique and its output is further processed by the XGBoost model for prediction of system status. The resulting predictive model has been shown to provide signals of potential failures up to seven days in advance, with an F1 score greater than 0.71 on the test set. In addition to the data-driven modeling approach, The present study also in- corporates model-free reinforcement learning (RL) algorithms to aid in decision-making in production optimization. The task of determining the optimal injection strategy poses challenges due to the complexity of the underlying dynamics, including nonlinear formulation, temporal variations, and reservoir heterogeneity. To tackle these challenges, the problem was reformulated as a Markov decision process and RL algorithms were employed to determine actions that maximize production yield. The results of the study demonstrate that the RL agent was able to significantly enhance the net present value (NPV) by continuously interacting with the environment and iteratively refining the dynamic process through multiple episodes. This showcases the potential for RL algorithms to provide effective and efficient solutions for complex optimization problems in the production domain. In conclusion, this study represents an original contribution to the field of data-driven applications in subsurface energy systems. It proposes a data-driven method for determining multi-phase flow rates in electrical submersible pumped (ESP) wells utilizing sensor measurements. The methodology includes conducting exploratory data analysis, conducting experiments to prioritize features, and evaluating models based on mean absolute error, mean squared error, and coefficient of determination. The findings indicate that a convolutional neural network-long short-term memory (CNN-LSTM) network is an effective approach for time series analysis in ESPs. In addition, the study implements principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to perform predictive maintenance on ESPs and anticipate potential failures up to a seven-day horizon. Furthermore, the study applies model-free reinforcement learning (RL) algorithms to aid decision-making in production optimization and enhance net present value (NPV)

Signal Based Data Mining For Feature Extraction And Fault Detection

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2012Bu çalışmada, işaret işleme teknikleri ve veri madenciliği yöntemleri kullanılarak özellik çıkarımı ve hata tespiti için yeni bir yöntem geliştirilmiştir. Uygulama ana olarak iki aşamadan oluşmaktadır. Bunlar sırasıyla veri ön-işleme ve yapay sinir ağı aşamalarıdır. Veri ön-işleme aşamasında asenkron bir elektrik motorunun sağlam durumundaki titreşim işareti ham veri olarak kullanılmıştır. Bu aşamada işarete ilk olarak sürekli dalgacık dönüşümü ve Fourier dönüşümü (Güç spektrumu yoğunluğu - GSY) teknikleri uygulanmıştır. Frekans domeninde temsil edilen bu yeni alt-işaret, yapay sinir ağı aşamasında bir öz-ilişkili yapay sinir ağının eğitimi için kullanılmıştır. Eğitim aşamasından sonra benzer veri kümesi ağın geri çağrılmasında kullanılmış ve bu sayede bir arıza eşik değeri belirlenmiştir. Bu yapay sinir ağı, sağlam durumdaki titreşim işaretinin güç spektrumu yoğunluğu ile test edilmiş ve eşik değerini aşan değerler arıza olarak değerlendirilmiştir. Bu model, yapay sinir ağı test aşamasında elde edilen sonuçlarla karşılaştırılıp özellik çıkarımı ve potansiyel hata tespiti açısından yorumlanmıştır. Buna ek olarak yöntem titreşim işaretinin haricinde iki adet simulasyon verisinde de uygulanmıştır. Sonuçlara bakıldığında yöntemin kullanılan tüm veriler için potansiyel arızaları belirlediği gözlenmiştir. Son olarak, çalışmada verilen işlem ve tekniklerin bir kullanıcı tarafından gerçekleştirilebilmesi amacıyla bir bilgisayar uygulaması oluşturulmuştur.In this study, a new method for fault detection and feature extaction is introduced by using signal processing and data mining techniques. The application is consisted of two main phases: data pre-processing and artificial neural network. Vibration signal measurements from the healthy state of an electric motor is used as the raw data for the application. At the data pre-processing phase, continuous wavelet transform and Fourier transform techniques are applied to the vibration signal. At the artificial neural network phase, this sub-signal is given as an input to an auto-associative neural network for training. After the training, the neural network is recalled by the same type of data for identifying a threshold value. At the test phase, the network is tested by the Fourier transform of the signal. According to the comparison with the threshold values, the faulty states are identified. The results are concluded in terms of feature extraction and fault detection of potential defects. In addition to this, the method is applied on two different types of simulation data. The method is capable of identifying the potential defects and faults for all the data provided. Finally, a computer application is developed in order to perform the method.DoktoraPh

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

SIMILARITY-BASED MULTI-SOURCE TRANSFER LEARNING APPROACH FOR TIME SERIES CLASSIFICATION

This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM). Conventional machine learning (ML) algorithms are widely adopted in PdM, however, most existing methods assume that the training (source) and testing (target) data follow the same distribution, and that labeled data are available in both source and target domains. For real-world PdM applications, the heterogeneity in machine original equipment manufacturers (OEMs), operating conditions, facility environment, and maintenance records collectively lead to heterogeneous distribution for data collected from different machines. This will significantly limit the performance of conventional ML algorithms in PdM. Moreover, labeling data is generally costly and time-consuming. Finally, industrial processes incorporate complex conditions, and unpredictable breakdown modes lead to extreme complexities for PdM. In this study, similarity-based multi-source transfer learning (SiMuS-TL) approach is proposed for real-time classification of time series signals. A new domain, called "mixed domain," is established to model the hidden similarities among the multiple sources and the target. The proposed SiMuS-TL model mainly includes three key steps: 1) learning group-based feature patterns, 2) developing group-based pre-trained models, and 3) weight transferring. The proposed SiMuS-TL model is validated by observing the state of the rotating machinery using a dataset collected on the Skill boss manufacturing system, publicly available standard bearing datasets, Case Western Reserve University (CWRU), and Paderborn University (PU) bearing datasets. The results of the performance comparison demonstrate that the proposed SiMuS-TL method outperformed conventional Support Vector Machine (SVM), Artificial Neural Network (ANN), and Transfer learning with neural networks (TLNN) without similarity-based transfer learning methods

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

APPLICATION OF SENSOR FUSION FOR SI ENGINE DIAGNOSTICS AND COMBUSTION FEEDBACK

Shifting consumer mindsets and evolving government norms are forcing automotive manufacturers the world over to improve vehicle performance and also reduce greenhouse gas emissions. A critical aspect of achieving future fuel economy and emission targets is improved powertrain control and diagnostics. This study focuses on using a sensor fusion based approach to improving control and diagnostics in a gasoline engine. A four cylinder turbocharged engine was instrumented with a suite of sensors including ion sensors, exhaust pressure sensors, crank position sensors and accelerometers. The diagnostic potential of these sensors was studied in detail. The ability of these sensors to detect knock, misfires and also correlate with pressure and combustion metrics was also evaluated. Lastly a neural network based approach to combine individual sensor signal information was developed. The neural network was used to estimate mean effective pressure and location of fifty percent mass fraction fuel burn. Additionally, the influence of various neural network architectures was studied. Results showed that under pseudo transient conditions a recursive neural network could use information from the low cost sensors to estimate mean effective pressure within an error of 0.1bar and combustion phasing within 2.5 crank-angle degrees