首页|Studies Conducted at Saveetha University on Machine Learning Recently Reported ( An Integrated Energy Storage Framework With Significant Energy Management and Ab sorption Mechanism for Machine Learning Assisted Electric Vehicle Application)
Studies Conducted at Saveetha University on Machine Learning Recently Reported ( An Integrated Energy Storage Framework With Significant Energy Management and Ab sorption Mechanism for Machine Learning Assisted Electric Vehicle Application)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Machine Lea rning. According to news originating from Tamil Nadu, India, by NewsRx correspon dents, research stated, "Regarding environmental friendliness, low maintenance n eeds, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) hav e become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energ y flow system." Our news journalists obtained a quote from the research from Saveetha University , "As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when c ompared to solo energy sources. Additionally, the primary problem interaction ap plications, including such battery electric vehicles, are the energy storage sys tem. Multiple energy storage technologies, including battery packs, flywheels, s uper-capacitors and fuel cells, are combined into a HESS due to their complement ing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing ba ckup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of po wer density. HESS needs a smart Energy Management System (EMS) to function prope rly since it combines the dynamic characteristics of a battery and a SuperCapaci tor (SC). The motive of the study is to suggest an actual power management contr ol system to accomplish these objectives. The plan is built using a wavelet tran sform, deep learning mechanism, and fuzzy logic together. A useful tool for sepa rating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is ch allenging to immediately apply it in a system, though. Because of this, the trad itional optimizationmodel- based facility energy management system encounters su bstantial difficulties with online forecast and calculation. To solve this probl em, the paper proposes a ML technique dependent on a Long ShortTerm Memory (LSTM ). The suggested control system structure allows for the separation of the offli ne and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline sta ge. As a result, the supercapacitor receives an online calculation and distribut ion of the high-frequency power requirement. The SOC of the supercapacitor is ke pt within the appropriate range via fuzzy logic control."
Tamil NaduIndiaAsiaCyborgsEmergi ng TechnologiesFuzzy LogicMachine LearningSaveetha University
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