Engineering Commons^™

Machine Learning Approach To Investigate Ev Battery Characteristics, Shayan Falahatdoost

Major Papers

The main factor influencing an electric vehicle’s range is its battery. Battery electric vehicles experience driving range reduction in low temperatures. This range reduction results from the heating demand for the cabin and recuperation limits by the braking system. Due to the lack of an internal combustion engine-style heat source, electric vehicles' heating system demands a significant amount of energy. This energy is supplied by the battery and results in driving range reduction. Moreover, Due to the battery's low temperature in cold weather, the charging process through recuperation is limited. This limitation of recuperation is caused by the low reaction …

Open-Circuit Voltage Models For Battery Management Systems: A Review, Prarthana Pillai, Sneha Sundaresan, Pradeep Kumar, Krishna R. Pattipati, Balakumar Balasingam

Mechanical, Automotive & Materials Engineering Publications

A battery management system (BMS) plays a crucial role to ensure the safety, efficiency, and reliability of a rechargeable Li-ion battery pack. State of charge (SOC) estimation is an important operation within a BMS. Estimated SOC is required in several BMS operations, such as remaining power and mileage estimation, battery capacity estimation, charge termination, and cell balancing. The open-circuit voltage (OCV) look-up-based SOC estimation approach is widely used in battery management systems. For OCV lookup, the OCV–SOC characteristic is empirically measured and parameterized a priori. The literature shows numerous OCV–SOC models and approaches to characterize them and use them in …

The Applications Of Blockchain Technologies To Electricity Markets, David Bowker, Vladislav Berezovsky, Marko Vukobratović, Santosh Jain, Subhendu Mukherjee, Fazel Mohammadi, Hannes Agabus

Electrical and Computer Engineering Publications

This paper is a summary of the CIGRE Technical Brochure 824 The Role of Blockchain Technologies in Power Markets [17]. The work of the contributors to the Technical Brochure is recognised. It is proposed to follow up this work in a new working group with a more in-depth look at the potential applications for blockchain in the area of energy trading.

Resilient Consensus Control Design For Dc Microgrids Against False Data Injection Attacks Using A Distributed Bank Of Sliding Mode Observers, Yousof Barzegari, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif, Vasile Palade

Electrical and Computer Engineering Publications

This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with …

Multi-Agent Based Protection Scheme Using Current-Only Directional Overcurrent Relays For Looped/Meshed Distribution Systems, Mohammad Ali Ataei, Mohsen Gitizadeh, Matti Lehtonen, Roozbeh Razavi-Far

Electrical and Computer Engineering Publications

The complexity of the design of the protection system using directional over current relays, for modern power distribution systems has been increased due to the looped/meshed operation and the penetration of distributed generations. Finding a reliable and efficient protection scheme that can be easily implemented in these distribution systems is a major challenge. An efficient solution could be the use of artificial intelligent-based multi-agent systems. This paper proposes a novel distributed intelligent based multi-agent protection scheme, which makes use of current-only directional over current relays as agents for detecting and locating faults and isolating faulty areas (lines/busbars) in the distribution …

Case Study Of Tv Spectrum Sensing Model Based On Machine Learning Techniques, Abdalaziz Mohammad, Faroq Ali Awin, Esam Abdel-Raheem

Electrical and Computer Engineering Publications

Spectrum sensing is an essential component in cognitive radios (CR). Machine learning (ML) algorithms are powerful techniques for designing a promising spectrum sensing model. In this work, the supervised ML algorithms, support vector machine (SVM), k-nearest neighbor (kNN), and decision tree (DT) are applied to detect the existence of primary users (PU) over the TV band. Moreover, the Principal Component Analysis (PCA) is incorporated to speed up the learning of the classifiers. Furthermore, the ensemble classification-based approach is employed to enhance the classifier predictivity and performance. Simulation results have shown that the highest performance is achieved by the ensemble classifier. …

Operating Room Scheduling Optimization Based On A Fuzzy Uncertainty Approach And Metaheuristic Algorithms, P Maghzi, M Mohammadi, S H.R Pasandideh, B Naderi

Electrical and Computer Engineering Publications

Today, planning and scheduling problems are the most significant issues in the world and make a great impact on improving organizational productivity and serving systems such as medical and healthcare providers. Since operating room planning is a major problem in healthcare organizations, the optimization of medical staff and equipment plays an essential role. Thus, this study presents a multi-objective mathematical model with a new categorization (preoperative, intraoperative, and postoperative) to minimize operating room scheduling and the risk of using equipment. Time constraints in healthcare systems and medical equipment limited capacity are the most significant considered limitation in the present study. …

Heterogeneous Collaborative Mapping For Autonomous Mobile Systems, Sooraj Sunil

Electronic Theses and Dissertations

An accurate map of the environment is essential for autonomous robot navigation. During collaborative simultaneous localization and mapping, the individual robots usually represent the environment as probabilistic occupancy grid maps. These maps can be exchanged among robots and fused to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such fusion is challenging due to the unknown initial correspondence problem. This thesis presents a novel feature-based map fusion approach through detecting, describing, and matching geometrically consistent features present in the overlapping region between the maps. The main drawback of usual feature-based approaches is the incapability …

Robot Learning From Human Observation Using Deep Neural Networks, Michael Elachkar

Electronic Theses and Dissertations

Industrial robots have gained traction in the last twenty years and have become an integral component in any sector empowering automation. Specifically, the automotive industry implements a wide range of industrial robots in a multitude of assembly lines worldwide. These robots perform tasks with the utmost level of repeatability and incomparable speed. It is that speed and consistency that has always made the robotic task an upgrade over the same task completed by a human. The cost savings is a great return on investment causing corporations to automate and deploy robotic solutions wherever feasible.

The cost to commission and set …

State Of Energy Estimation Of Li-Ion Batteries Using Deep Neural Network And Support Vector Regression, Pradeep Kumar, Yasser Rafat, Paolo Cicconi, Mohammad Saad Alam

Mechanical, Automotive & Materials Engineering Publications

Efficient management of the power and energy output of a high voltage battery pack requires a precise estimation of the State of Energy (SOE). For the accurate estimation of SOE, this work presents two data-driven methods as Deep Neural Network (DNN) and a regression model, i.e. Support Vector Regression (SVR). The effectiveness of the SOE estimation was compared, analysed, and studied through these models under similar conditions. For performance enhancement of estimation, a modified algorithm based on the grid search of optimized hyperparameters was proposed and evaluated in both the models. For training of the model at subsequent thermal ranges, …

A Critical Study On The Effect Of Dimensionality Reduction On Intrusion Detection In Water Storage Critical Infrastructure, Ranim Aljoudi

Electronic Theses and Dissertations

Supervisory control and data acquisition (SCADA) systems are often imperiled bycyber-attacks, which can often be detected using intrusion detection system (IDSs).However, the performance and efficiency of IDSs can be affected by several factors,including the quality of data, curse of dimensionality of the data, and computationalcost. Feature reduction techniques can overcome most of these challenges by eliminatingthe redundant and non-informative features, thereby increasing the detectionaccuracy. This study aims to shows the importance of feature reduction on the intrusiondetection performance. To do this, a multi-modular IDS is designed that isconnected to the SCADA system of a water storage tank. A comparative study …

Design, Analysis And Fabrication Of Capacitive Micromachined Resonator – Based Mass Sensors, Muhammed Umair Nathani

Electronic Theses and Dissertations

A challenge in greenhouses is the presence of various pests, virus, and bacteria. Although many pest management strategies are available, however, they all depend on visually identifying these invasive forces when they have eradicated the crop. To avoid the impacts on the agricultural sector due to such pests, early detection is required. Therefore, in this thesis MEMS-based capacitive mass resonators are proposed for early detection of such invasive forces through identifying their released volatile organic compounds (VOCs). In this work, multiple moving membrane capacitive micromachined ultrasonic transducer (M3-CMUT) as a mass sensor is proposed due to its advantages shared with …

Skincan Ai: A Deep Learning-Based Skin Cancer Classification And Segmentation Pipeline Designed Along With A Generative Model, Shivang Rana

Electronic Theses and Dissertations

The rarity of Melanoma skin cancer accounts for the dataset collected to be limited and highly skewed, as benign moles can easily mimic the impression of the melanoma-affected area. Such an imbalanced dataset makes training any deep learning classifier network harder by affecting the training stability. We have an intuition that synthesizing such skin lesion medical images could help solve the issue of overfitting in training networks and assist in enforcing the anonymization of actual patients. Despite multiple previous attempts, none of the models were practical for the fast-paced clinical environment. In this thesis, we propose a novel pipeline named …

Enhancing Multi-View 3d-Reconstruction Using Multi-Frame Super Resolution, Michael Lee

Electronic Theses and Dissertations

Multi-view stereo is a popular method for 3D-reconstruction. Super resolution is a technique used to produce high resolution output from low resolution input. Since the quality of 3D-reconstruction is directly dependent on the input, a simple path is to improve the resolution of the input.

In this dissertation, we explore the idea of using super resolution to improve 3D-reconstruction at the input stage of the multi-view stereo framework. In particular, we show that multi-view stereo when combined with multi-frame super resolution produces a more accurate 3D-reconstruction.

The proposed method utilizes images with sub-pixel camera movements to produce high resolution output. …

Surrogate Modeling Of Fluid Flows With Physics-Aware Graph Neural Networks, Emanuel Raad

Electronic Theses and Dissertations

Graph neural networks provide a framework for learning on unstructured data, such as meshes used for solving Computational Fluid Dynamics problems. However, current applications do not take advantage of known physical laws in the training process. This thesis addresses that gap by introducing graph convolution layers to calculate the divergence and gradient operator. The convolutions are valid on any 2D or 3D graph storing spatial data, and can be added to existing graph architectures. Using these convolutions, the residuals of the conservation of mass and momentum equations are computed and minimized through a physics-aware loss function. Two classical fluid dynamics …

Efficient Evaluation Of Probability And Reliability With Digital Integrated Circuits, Suoyue Zhan

Electronic Theses and Dissertations

As complementary metal–oxide–semiconductor (CMOS) devices shrink to nanoscale, digital integrated circuits (ICs) are more susceptible to various environmental parameters, such as temperature, supply voltage, wiring, noise, and fabrication process variations. This would reduce the circuit operation reliability (i.e., the probability that a circuit or component is performing its intended logic function). Signal probability (the probability that a digital signal is producing logic 1) is another factor that measures circuit’s dynamic behavior and power dissipation. Research shows that signal probability and reliability within ICs may interact with each other in a complicated way. Generally speaking, as signal probability changes due to …