EV Misalignment Estimation in DWPT Systems Utilizing the Roadside Charging Pads

• Published in: IEEE transactions on transportation electrification 2022-03, Vol.8 (1), p.752-766
• Main Authors: Tavakoli, Reza, Shabanian, Tahmoures, Dede, Ercan M., Chou, Chungchih, Pantic, Zeljko
• Format: Article
• Language: English
• Subjects: Artificial neural network (ANN); Artificial neural networks; Coils; dynamic wireless power transfer (DWPT); electric vehicle (EV); Electric vehicles; Estimation; Inductance; lateral misalignment (LTM); Magnetic fields; magnetic sensor; Misalignment; misalignment estimation; Receivers; Roads; Roadsides; sensing coil; Sensors; Vehicle dynamics; vertical misalignment (VM); wireless charging; Wireless power transmission
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2021.3091969

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.