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• W4313563356 abstract "This research intends to reduce an electric vehicle’s (EV) losses and increase its driving range by implementing a hybrid Energy Management Strategy (EMS). Various experimental procedures are used to determine the precise characteristics of an Interior Permanent Magnet Synchronous Motor (IPMSM). An Artificial Neural Network (ANN) is formed to replicate the motor-inverter system. The longitudinal model of the vehicle is used to quantity the necessary kinetic energy on the axle to drive the automobile. A Genetic Algorithm (GA) is used in this study to determine the optimal operating criteria which need minimal force on the axles and ensure reduced power loss in the EV’s electronics. When compared to the most frequently investigated driving cycle, WLTP, the outcome is an EMS that optimizes speed profile and saves energy by 9%." @default.
• W4313563356 created "2023-01-06" @default.
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• W4313563356 date "2022-11-01" @default.
• W4313563356 modified "2023-10-10" @default.
• W4313563356 title "A Hybrid Energy Management Strategy Based on ANN and GA Optimization for Electric Vehicles" @default.
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• W4313563356 doi "https://doi.org/10.1109/vppc55846.2022.10003310" @default.
• W4313563356 hasPublicationYear "2022" @default.
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