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    Identification of Material Parameters in Lithium-ion Batteries
    (2023) Talware, Pranav; Bijalwan, Aarushi; Chalmers tekniska högskola / Institutionen för elektroteknik; Thiringer, Torbjörn
    Abstract As the world is shifting towards electric vehicles, there is a need for high performing batteries which can be achieved by studying the material parameters in a battery cell in order to understand its behavior. In this thesis work, half cells and 3-electrode cells are assembled with cell chemistries NMC111, NMC622 and NMC811 each as cathodes. Electrochemical techniques are used to estimate material parameters. Galvanostatic Intermittent Titration Technique(GITT) is performed on half cells and three electrode cells. Three methods are used to analyse the GITT results and obtain a range of diffusion coefficients. Electrochemical Impedance Spectroscopy (EIS) is performed on a 3-electrode cell, and the measurement is done between cathode and Li-reference ring. The results of the EIS are analysed using an equivalent circuit model resembling a physical cell. Diffusion coefficients are calculated from both GITT and EIS and a comparative study is presented for each cell chemistry. Some other parameters like the double layer capacitance, exchange current density and cathode electrolyte interface capacitance are also estimated from EIS and a comparative study between different cell chemistries is presented. The diffusion coefficients from GITT are in the order of 10−15 [m2/s] whereas from the EIS test, it is in the order of 10−12[m2/s].
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    Energy storage systems to provide ancillary services to the power system
    (2023) Figueras Llerins, Lluc; Chalmers tekniska högskola / Institutionen för elektroteknik; Bongiorno, Massimo; Narula, Anant
    Abstract The power system has experienced a significant transformation in the last years, notably due to the large increase of power electronic-based technologies. These new technologies are mostly based on converter-interfaced generation (CIG), which have different dynamic response characteristics than classical synchronous generators. Power systems with high penetration of CIG are characterized by low grid inertia due to the lack of frequency containment provided by synchronous generators. This thesis discusses integrating energy storage systems into the power system to overcome the challenges caused by the increasing penetration of CIGs. Different types of energy storage technologies are studied, and recommendations on the size of energy storage systems are proposed based on the functionalities to be implemented. Moreover, modelling and control methods for Battery and Supercapacitor Energy Storage Systems integrated with grid-connected converters are presented. These grid-connected converters use grid-forming control strategies to provide functionalities of the synchronous generators, such as inertial response. Different grid-forming control strategies are presented and compared. Finally, a dc-link voltage based synchronization control is proposed. The dc-link voltage control utilized the dc-link dynamics to synchronize the grid-forming converter with the grid. Time domain simulations in Simulink and PSCAD are performed to analyze the different controllers and the system’s dynamic behaviour.
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    DC Line Fault Prognosis Using Deep Recurrent Neural Network Over Sensor Data
    (2023) Vissakodeti, Akhil Venkat; Chalmers tekniska högskola / Institutionen för elektroteknik; Hammarström, Thomas
    Abstract The HVDC technology has become prominent because of its increased long-distance bulk power transmission efficiency and facilitation of asynchronous interconnections. The loaded cable can, however, fail due to flashover or short circuit in the power system. As a result, this can cause a grid failure and damage the equipment by introducing a high level of current in the system. To detect fault is therefore considered a cost-efficient and non-destructive technique to monitor the cable operating condition. The main aim of this thesis is to predict faults in a DC cable using measured data from the sensors present in the system. Moreover, this method helps to identify the cable fault before power failure with possible catastrophic consequences occurs. This thesis examines the prospect of employing deep neural networks to capture the hidden patterns from the time series sensors to predict DC cable fault at early stages. This is justified because deep learning approaches are well suited to incorporating feature extraction into the predictive model. In this regard, long short-term memory (LSTM) is considered to get a remarkable accuracy of 99.93%. A lower Relative value of the absolute error of the signals proves that the model predicts the accurate results for the fixed window size.
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    Analysis of numerical methods for data driven regression problems in neural networks
    (2023) Anastasiadis, Apostolos; Chalmers tekniska högskola / Institutionen för elektroteknik; Kulcsár, Balázs Adam
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    Identification of Uncertainties and Disturbances for Safe Autonomous Driving Control
    (2023) Singh, Ranbir; Chalmers tekniska högskola / Institutionen för elektroteknik; Murgovski, Nikolce