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crossrefEnergies2024-08-06Cited by 13

Optimal Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicle Applications Using a Machine Learning-Based Surrogate Model

Song Guo, Xiangdong Su, Hang Zhao

This paper presents an innovative design for an interior permanent magnet synchronous motor (IPMSM), targeting enhanced performance for electric vehicle (EV) applications. The proposed motor features a double V-shaped rotor structure with irregular ferrite magnets embedded in the slots between the permanent magnets. This design significantly enhances torque performance. Furthermore, a machine learning-based surrogate model is developed by integrating fine and coarse mesh data. Optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this surrogate model effectively reduces computational time compared to traditional finite element analysis (FEA).

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crossrefEnergies2022-02-09Cited by 45

Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms

Paweł Piotrowski, Dariusz Baczyński, Marcin Kopyt, Tomasz Gulczyński

The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 2…

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crossrefEnergies2021-09-28Cited by 82

Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation

Mostafa Shibl, Loay Ismail, Ahmed Massoud

Electric vehicles (EVs) have gained in popularity over the years. The charging of a high number of EVs harms the distribution system. As a result, increased transformer overloads, power losses, and voltage fluctuations may occur. Thus, management of EVs is required to address the…

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crossrefEnergies2023-12-08Cited by 4

Determination of Energy Savings via Fuel Consumption Estimation with Machine Learning Methods and Rule-Based Control Methods Developed for Experimental Data of Hybrid Electric Vehicles

Yılmaz Seryar Arıkuşu, Nevra Bayhan, Hasan Tiryaki

In this study, a parallel hybrid electric vehicle produced within the scope of our project titled “Development of Fuel Efficiency Enhancing and Innovative Technologies for Internal Combustion Engine Vehicles” has been modeled. Firstly, a new rule-based control method is proposed…

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crossrefEnergies2024-05-31Cited by 1

Novel Machine Learning Control for Power Management Using an Instantaneous Reference Current in Multiple-Source-Fed Electric Vehicles

G. Mathesh, Raju Saravanakumar, Rohit Salgotra

Using multiple input power sources increases the reliability of electric vehicles compared to a single source. However, the inclusion of other sources exhibits complexity in the controller system, such as computing time, program difficulty, and switching speed to connect or disco…

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crossrefEnergies2023-06-11Cited by 8

On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles

Edoardo Lelli, Alessia Musa, Emilio Batista, Daniela Anna Misul, Giovanni Belingardi

The present study investigates the use of machine learning algorithms to estimate the state of health (SOH) of high-voltage batteries in electric vehicles. The analysis is based on open-circuit voltage (OCV) measurements from 12 vehicles with different mileage conditions and focu…

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crossrefEnergies2021-01-22Cited by 193

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Tianze Lan, Kittisak Jermsittiparsert, Sara T. Alrashood, Mostafa Rezaei, Loiy Al-Ghussain, Mohamed A. Mohamed

Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse…

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