2022 journal article
EV Misalignment Estimation in DWPT Systems Utilizing the Roadside Charging Pads
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 8(1), 752–766.
author keywords: Coils; Estimation; Sensors; Roads; Vehicle dynamics; Inductance; Receivers; Artificial neural network (ANN); dynamic wireless power transfer (DWPT); electric vehicle (EV); lateral misalignment (LTM); magnetic sensor; misalignment estimation; sensing coil; vertical misalignment (VM); wireless charging
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(Web of Science)
Source: Web Of Science
Added: May 23, 2022
This article presents a magnetic field-based method for vehicle misalignment estimation in dynamic wireless power transfer (DWPT) systems. Road-embedded Transmitter (Tx) pads are employed to both charge a moving electric vehicle (EV) and participate in the estimation of the EV lateral and vertical misalignments (VMs). The EV is equipped with a power receiver (Rx) pad and four sensing coils. The proposed system exploits the existing road-embedded pads to generate a magnetic field for misalignment estimation. The sensing coils measure the magnetic field produced by Tx pads. These measurements are uniquely affected by EV lateral and VMs. Misalignments are estimated by analyzing the voltage induced in sensing coils. Six artificial neural networks (ANNs) are employed to calculate misalignment values in real time and report them to the EV control system or the vehicle driver. To demonstrate the system practicality, a 500-W DWPT prototype consisting of two Tx pads is developed and tested. Misalignment estimation is tested in the range [−15, 15] cm laterally and [16.5, 22.5] cm vertically, and the estimation error is identified to be less than 1.9% and 3.8% of the full misalignment ranges, respectively.