Work

EV Heavy Duty Truck Dynamic Wireless Power Transfer Design Optimization Environment

Year: 2026

Type: article

Abstract: This paper presents a design optimization environment for Electric Vehicle Heavy-Duty Truck (EV-HDT) Dynamic Wireless Power Transfer (DWPT) charging systems. It employs Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANNs), where the ANN acts as a system identifier trained offline using data from 3D Finite Element and State-Space (FE-SS) models and Taguchi algorithms. The study investigates a multi-objective optimization strategy based on Goal Programming that accounts for vehicle motion by integrating a Probability Density Function (PDF) of lateral misalignment into a stochastic objective function to represent realistic driving conditions. In addition, it demonstrates that the use of an ANN system identifier in conjunction with Taguchi algorithms reduced the computation time by more than 90%.

Source: IEEE Transactions on Magnetics

DOI: 10.1109/tmag.2026.3726824

Authors: Rakan Almazmomi, A.A. Arkadan

Institutions: King Abdulaziz University, Colorado School of Mines

Language: English

Topic: Wireless Power Transfer Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $2,645