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Rechargeable Batteries State Estimation Techniques Based on Adaptive- Rate Processing and Machine Learning in the Renewable Energy and Sustainability Prospect
alyousef, afnan
alyousef, afnan
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The integrated power system technologies have been evolved in recent technology. The developments of smart and microgrids need to use lithium-ion batteries. 9 The battery is a vital component of contemporary power systems and is used in a variety of important applications. like hybrid cars, drones, avionics, satellites, mobile phones, and energy storage for renewable microgrids. " lithium-ion batteries are widely used because of their excellent properties such as high energy density, installation size, self-discharge, and high-power supply capacity. Batteries are quite expensive, therefore for an effective utilization of batteries and in order to assure their longer life the Battery Management Systems (BMSs) are frequently employed. The modern BMSs require extensive processing resources which can render into higher power consumption overhead. In this context, several embedded and integrated systems-based solutions have been proposed. The first step of this thesis will be to identify and employ an appropriate existing high-power lithium-ion Battery real dataset. In the second step, this dataset will be processed to make its format compatible with MATLAB. The proposed system will be designed in MATLAB. The event-driven peak sensing models will be used for the effective extraction of the features from the intended battery consumption parameters. It will be mainly developed on the principle of the signal-driven phenomenon. This work is well aligned with the 2030 vision of Saudi Arabia and the goals of future smart cities like NEOM. It contributes in realizing modern smart – energy related services such as electric vehicles, hybrid power systems, integration of renewable energy sources in smart grid, mitigation of power quality issues, effective dimensioning of renewable energy systems, efficient cell-balancing, and energy storage automatic management and maintenance.