Novel Electrical Modeling, Design and Comparative Control Techniques for Wireless Electric Vehicle Battery Charging
Dynamic wireless power systems are an effective way to supply electric vehicles (EVs) with the required power while moving and to overcome the problems of low mileage and extensive charging times. This paper targets modeling and control for future dynamic wireless charging using magnetic resonance c...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/22/2842 |
| Summary: | Dynamic wireless power systems are an effective way to supply electric vehicles (EVs) with the required power while moving and to overcome the problems of low mileage and extensive charging times. This paper targets modeling and control for future dynamic wireless charging using magnetic resonance coupling because of the latter’s efficiency. We present a 3D model of transmitter and receiver coils for EV charging with magnetic resonance wireless power developed using ANSYS Maxwell. This model was incorporated into the physical design of the magnetic resonance coupling using ANSYS Simplorer in order to optimize the power. The estimated efficiency was around 92.1%. The transient analysis of the proposed circuit was investigated. A closed-loop three-level cascaded PI controller- was utilized for wireless charging of an EV battery. The controller was designed to eliminate the voltage variation resulting from the variation in the space existing between coils. A single-level PI controller was used to benchmark the proposed system’s performance. Furthermore, solar-powered wireless power transfer with a maximum power point tracker was used to simulate the wireless charging of an electric vehicle. The simulation results indicated that the EV battery could be charged with a regulated power of 12 V and 5 A through wireless power transfer. Fuzzy logic and neuro-fuzzy controllers were employed for more robustness in the performance of the output. The neuro-fuzzy controller showed the best performance in comparison with the other designs. All the proposed systems were checked and validated using the OPAL Real-Time simulator. The stability analysis of the DC–DC converter inside the closed-loop system was investigated. |
|---|---|
| ISSN: | 2079-9292 |