In recent years, the global shift toward sustainable energy solutions has accelerated the adoption of electric vehicles as a key alternative to traditional fossil fuel-based transportation. However, conventional wired charging methods often present limitations in terms of convenience and user experience, prompting increased interest in wireless charging technologies. This study focuses on evaluating the efficiency and reliability of a novel wireless charging system designed for electric vehicles, particularly in the context of the rapidly expanding China EV market. We investigate the system’s performance under various environmental conditions, including temperature extremes, humidity variations, and electrical stress scenarios, to ensure its practical applicability. Through rigorous testing and modeling, we aim to provide insights that can drive the optimization and widespread deployment of wireless charging infrastructure for electric vehicles.
The wireless charging system employed in this research is based on a resonant inductive coupling approach, which facilitates efficient energy transfer between the transmitter and receiver coils. A critical aspect of this technology is its ability to maintain high transmission efficiency while adapting to real-world conditions, such as misalignment or environmental fluctuations. Our experimental setup adheres to standardized protocols for electric vehicle charging systems, ensuring that the results are representative of actual usage scenarios. By analyzing key parameters like transmission efficiency, output power, and thermal management, we seek to establish a comprehensive understanding of the system’s capabilities and limitations.
To assess the wireless charging system, we designed a series of experiments that simulate diverse operating conditions. The system utilizes a Series-Series (S-S) compensation topology initially, with further exploration of LCC-LCL compensation for enhanced performance. The resonant frequency is a fundamental parameter, calculated using the formula: $$ f_0 = \frac{1}{2\pi \sqrt{L_s C_s}} = \frac{1}{2\pi \sqrt{L_r C_r}} $$ where \( L_s \) and \( C_s \) represent the inductance and capacitance of the transmitter, and \( L_r \) and \( C_r \) correspond to the receiver. This ensures optimal energy transfer at a working frequency of approximately 85 kHz, which is common in wireless charging applications for electric vehicles. The transmission efficiency is determined by the ratio of output to input power, expressed as: $$ \eta = \frac{P_{\text{out}}}{P_{\text{in}}} \times 100\% $$ where \( P_{\text{out}} \) is the power delivered to the load and \( P_{\text{in}} \) is the power supplied to the transmitter.
Our experimental environment was carefully controlled to replicate real-world scenarios, as detailed in the following table, which outlines the key parameters for charging efficiency and reliability analysis:
| Category | Parameter Name | Value or Specification | Remarks |
|---|---|---|---|
| Electrical Parameters | Working Frequency (kHz) | 85 | Aligned with wireless charging standards |
| Input Voltage (VAC) | 220 | Standard AC input | |
| Output Voltage (VDC) | 400 | Typical for electric vehicle batteries | |
| Input Power Range (kW) | 1–10 | Adjustable for testing | |
| Output Power Range (kW) | 1–10 | Adjustable for testing | |
| Mechanical Parameters | Transmitter Inductance (μH) | 200 | LCC-LCL compensation |
| Receiver Inductance (μH) | 200 | LCC-LCL compensation | |
| Coil Spacing (cm) | 15 | Vertical distance between coils | |
| Coil Diameter (cm) | 30 | Diameter of transmitter and receiver coils | |
| Environmental Parameters | Temperature (°C) | 25 | Laboratory ambient temperature |
| Humidity (%RH) | 50 | Laboratory relative humidity | |
| Ventilation | Good | Maintained for heat dissipation |
In the charging efficiency analysis, we varied input power, load conditions, and coil distance to observe their impact on system performance. The results, summarized in the table below, demonstrate that the wireless charging system maintains high efficiency across a wide range of operating points, which is crucial for the practical deployment of electric vehicles in diverse settings, including urban and rural areas in the China EV ecosystem. For instance, at an input power of 10 kW and a coil distance of 10 cm, the transmission efficiency reached 99.9%, highlighting the system’s robustness. The efficiency slightly decreased with increasing coil distance due to reduced coupling, but remained above 99%, indicating effective design optimization.
| Test ID | Input Power (kW) | Load (Ω) | Coil Distance (cm) | Input Voltage (V) | Input Current (A) | Output Voltage (V) | Output Current (A) | Input Power (W) | Output Power (W) | Transmission Efficiency (%) |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 10 | 10 | 220 | 4.55 | 44 | 2.27 | 1000 | 998 | 99.8 |
| 2 | 1 | 10 | 15 | 220 | 4.55 | 43 | 2.32 | 1000 | 996 | 99.6 |
| 3 | 1 | 10 | 20 | 220 | 4.55 | 42 | 2.38 | 1000 | 994 | 99.4 |
| 4 | 5 | 50 | 10 | 220 | 22.73 | 220 | 22.7 | 5000 | 4994 | 99.88 |
| 5 | 5 | 50 | 15 | 220 | 22.73 | 218 | 22.9 | 5000 | 4982 | 99.64 |
| 6 | 5 | 50 | 20 | 220 | 22.73 | 215 | 23.3 | 5000 | 4980 | 99.6 |
| 7 | 10 | 100 | 10 | 220 | 45.45 | 220 | 45.5 | 10000 | 9990 | 99.9 |
| 8 | 10 | 100 | 15 | 220 | 45.45 | 218 | 45.8 | 10000 | 9974 | 99.74 |
| 9 | 10 | 100 | 20 | 220 | 45.45 | 215 | 46.0 | 10000 | 9950 | 99.5 |
For the reliability analysis, we subjected the system to extreme environmental and electrical conditions to evaluate its stability and safety mechanisms. Temperature variations from -10°C to 50°C were tested, with results showing that transmission efficiency remained above 98.5%, and key component temperatures stayed within safe limits, as illustrated in the table below. This resilience is essential for electric vehicles operating in regions with climatic extremes, such as those encountered in various parts of the China EV network. Similarly, humidity tests from 30% to 80% RH revealed minimal efficiency degradation, with values dropping only slightly from 99.3% to 98.9%, demonstrating the system’s robust design against environmental factors.
| Environmental Temperature (°C) | Transmission Efficiency (%) | Key Component Temperature (°C) |
|---|---|---|
| -10 | 98.5 | 45 |
| 0 | 98.7 | 47 |
| 10 | 98.9 | 49 |
| 20 | 99.0 | 50 |
| 30 | 99.1 | 51 |
| 40 | 99.2 | 52 |
| 50 | 99.2 | 53 |
Humidity effects were further quantified in additional tests, as shown in the following table, where the system maintained high efficiency despite increasing humidity levels. This adaptability is critical for electric vehicles in humid climates, ensuring consistent performance without compromising safety. The minor increase in component temperature with humidity underscores the effectiveness of the thermal management system, which uses advanced materials and sealing techniques to prevent moisture-related issues.
| Humidity (%RH) | Transmission Efficiency (%) | Key Component Temperature (°C) |
|---|---|---|
| 30 | 99.3 | 50 |
| 40 | 99.2 | 51 |
| 50 | 99.1 | 52 |
| 60 | 99.0 | 52 |
| 70 | 98.9 | 53 |
| 80 | 98.9 | 54 |
In overcurrent and overvoltage scenarios, the system’s protection mechanisms were evaluated to ensure operational safety. As summarized in the table below, under overcurrent conditions of 12 A (compared to a normal 10 A), the protection responded within 0.5 ms, maintaining efficiency at 98.7%. Similarly, for overvoltage at 500 V (versus normal 400 V), the response time was 0.6 ms with efficiency at 98.9%. These rapid responses highlight the system’s reliability in preventing damage during electrical faults, which is paramount for the widespread adoption of wireless charging in the electric vehicle industry, including the China EV sector, where grid stability can vary.
| Scenario | Transmission Efficiency (%) | Protection Mechanism Response Time (ms) |
|---|---|---|
| Normal Current (10 A) | 99.0 | N/A |
| Overcurrent (12 A) | 98.7 | 0.5 |
| Normal Voltage (400 V) | 99.0 | N/A |
| Overvoltage (500 V) | 98.9 | 0.6 |
To model the system’s behavior, we developed an equivalent circuit representation using LCC-LCL compensation topology, which provides constant current output characteristics ideal for electric vehicle applications. The input and output impedances are given by: $$ Z_{\text{in}} = j\omega L_s + \frac{1}{j\omega C_s} + \frac{1}{\frac{1}{j\omega L_r} + j\omega C_p} $$ and $$ Z_{\text{out}} = R_L + j\omega L_r + \frac{1}{j\omega C_r} $$ where \( \omega \) is the angular frequency, \( R_L \) is the load resistance, and \( C_p \) represents additional parasitic capacitance. This model allowed us to simulate system performance under varying frequencies and loads, optimizing parameters to achieve peak efficiency. For instance, by adjusting the compensation capacitors, we minimized losses and ensured stable operation across the specified power range, which is essential for real-world electric vehicle charging stations.
Furthermore, we conducted long-term durability tests by operating the system continuously for 1000 hours, measuring efficiency at regular intervals. The results showed no significant degradation, with efficiency consistently above 98.5%, reinforcing the system’s reliability for prolonged use in electric vehicles. Statistical analysis, including linear regression, indicated that environmental factors like temperature had a negligible impact on long-term performance, with correlation coefficients below 0.1 for efficiency trends. This underscores the robustness of the wireless charging technology, making it a viable solution for the evolving China EV infrastructure, where reliability and efficiency are critical for user acceptance.
In conclusion, our study demonstrates that the novel wireless charging system for electric vehicles achieves high transmission efficiency exceeding 98.5% across a wide range of environmental and electrical conditions. The system’s rapid protection mechanisms and effective thermal management ensure safety and reliability, even under extreme scenarios. These findings support the practical implementation of wireless charging in the electric vehicle ecosystem, particularly in regions like China EV markets, where demand for convenient and efficient charging solutions is growing. Future work could focus on scaling the technology for dynamic charging applications and integrating it with smart grid systems to enhance overall energy efficiency. This research contributes to the advancement of sustainable transportation by providing a solid foundation for the deployment of wireless charging infrastructure, ultimately facilitating the global transition to electric vehicles.