In recent years, the global shift toward sustainable energy solutions has accelerated, driven by the urgent need to reduce carbon emissions and combat climate change. Solar energy, as a low-cost and abundant renewable resource, has garnered significant attention for its potential to power various applications, including the rapidly growing electric car sector. The integration of photovoltaic (PV) systems with wireless charging technology offers a promising pathway to enhance the convenience and environmental benefits of electric vehicles (EVs), particularly in regions like China where the adoption of China EV is surging. This paper presents the design and implementation of an experimental platform for a PV-powered wireless charging system tailored for electric cars, focusing on the core components: PV generation with maximum power point tracking (MPPT) and inductive wireless power transfer (WPT) using an LCC-S compensation topology. We developed this platform to provide hands-on educational insights into power electronics, renewable energy systems, and wireless charging mechanisms, which are critical for advancing the China EV infrastructure. Through simulations and hardware experiments, we validated the system’s ability to efficiently track the maximum power point of PV modules and transfer power wirelessly to electric car batteries, demonstrating its practicality for real-world applications.
The overall structure of the PV-powered wireless charging system for electric cars comprises several key modules: the PV array, a DC-DC converter (specifically a Boost circuit) for voltage regulation, an MPPT controller implemented with a DSP28335 microcontroller, an inverter for AC conversion, a wireless charging unit with LCC-S compensation, and a receiver coil connected to the electric car battery. This integrated approach ensures that solar energy is harnessed optimally and delivered seamlessly to the EV, reducing reliance on grid power and enhancing sustainability. In the following sections, we delve into the theoretical foundations, simulation analyses, and experimental validations of each subsystem, emphasizing the role of advanced control algorithms and power electronics in achieving high efficiency. We also incorporate tables and equations to summarize key parameters and relationships, facilitating a deeper understanding of the system’s behavior. The growing demand for electric cars in markets like China EV underscores the importance of such innovations, as they can support the development of smart charging infrastructure and reduce the carbon footprint of transportation.
The PV generation system forms the foundation of this experimental platform, converting solar energy into electrical power for charging electric cars. We began by analyzing the working principle of PV cells, which generate current and voltage based on incident sunlight and temperature. The output characteristics of a PV module are non-linear and depend on environmental factors; thus, to maximize energy harvest, we employed an MPPT algorithm. In our setup, the PV module is connected to a Boost DC-DC converter, which adjusts the operating point of the PV array to track the maximum power point (MPP). The Boost converter operates by controlling the duty cycle of a pulse-width modulation (PWM) signal, effectively varying the equivalent resistance seen by the PV module. This allows the system to adapt to changing conditions, such as variations in solar irradiance and temperature, which are common in real-world scenarios for electric car charging stations, especially in diverse climates like those found in China EV deployments.
To model the PV system, we developed a simulation in MATLAB/Simulink, representing the PV array using a single-diode model. The I-V and P-V curves of the PV module were characterized to understand its behavior under different conditions. The MPPT algorithm we implemented is the perturb and observe (P&O) method, due to its simplicity and effectiveness. This algorithm works by periodically perturbing the PV voltage and observing the change in output power; if the power increases, the perturbation continues in the same direction; otherwise, it reverses. The flowchart of the P&O algorithm illustrates this iterative process, ensuring rapid convergence to the MPP even under dynamic conditions. For instance, in our simulations, we tested step changes in temperature and irradiance to mimic real-world variability. The results showed that the system could quickly track the MPP, with minimal power loss, making it suitable for integration with electric car charging systems where consistent energy delivery is crucial.
The mathematical representation of the Boost converter is essential for understanding how MPPT is achieved. The input-output voltage relationship is given by:
$$ U_o = \frac{U_i}{1 – D} $$
where \( U_i \) is the input voltage from the PV module, \( U_o \) is the output voltage, and \( D \) is the duty cycle of the PWM signal. Assuming ideal conditions, the input power equals the output power, leading to the equivalent resistance seen by the PV module:
$$ R_{eq} = (1 – D)^2 R $$
Here, \( R \) is the load resistance. By adjusting \( D \), the equivalent resistance changes, allowing the PV module to operate at its MPP. This principle is central to our MPPT implementation, as it enables efficient energy extraction for powering electric cars. In simulations, we varied irradiance from 1 kW/m² to 1.5 kW/m² and temperature from 25°C to 60°C, observing that the P&O algorithm maintained MPP tracking with minimal oscillation. The following table summarizes the key parameters used in the PV system simulation, highlighting the impact of environmental changes on performance, which is vital for designing reliable charging systems for the expanding China EV market.
| Parameter | Value | Description |
|---|---|---|
| PV Open-Circuit Voltage | 22 V | Voltage at zero current |
| PV Short-Circuit Current | 5 A | Current at zero voltage |
| MPP Voltage | 18 V | Voltage at maximum power |
| MPP Current | 4.5 A | Current at maximum power |
| Boost Switching Frequency | 50 kHz | PWM frequency for DC-DC converter |
| Simulated Irradiance Range | 1-1.5 kW/m² | Range for testing MPPT |
| Simulated Temperature Range | 25-60°C | Range for testing MPPT |
The wireless charging system for electric cars is designed based on the LCC-S compensation topology, which provides stable output characteristics and high efficiency. We chose this topology for its ability to maintain constant voltage output, which is beneficial for charging electric car batteries without overvoltage risks. The system consists of a transmitting side with an inverter, compensation network (including inductors and capacitors), and a transmitting coil, and a receiving side with a receiving coil, compensation network, and a rectifier connected to the battery load. The LCC-S topology ensures resonance at a specific frequency, minimizing reactive power losses and enhancing power transfer efficiency. In our analysis, we derived the mathematical model of the LCC-S circuit to understand its output behavior and optimize the design for electric car applications, considering factors like misalignment and varying load conditions common in China EV usage.
The equivalent circuit of the LCC-S topology includes the transmitting coil inductance \( L_T \), receiving coil inductance \( L_R \), mutual inductance \( M_{TR} \), and compensation capacitors \( C_T \), \( C_R \), and \( C_F \). The system operates at a resonant frequency \( \omega \), and the resonance conditions are given by:
$$ L_F C_F = L_R C_R = (L_T – L_F) C_T = \frac{1}{\omega^2} $$
Using fundamental harmonic analysis, the AC equivalent resistance \( R_{EQ} \) and the input/output voltage relationships are expressed as:
$$ R_{EQ} = \frac{8}{\pi^2} R_L $$
$$ U_T = \frac{2\sqrt{2}}{\pi} U_{INV} $$
$$ U_R = \frac{2\sqrt{2}}{\pi} U_{REC} $$
where \( R_L \) is the load resistance, \( U_{INV} \) is the inverter DC input voltage, and \( U_{REC} \) is the rectifier DC output voltage. Applying Kirchhoff’s voltage law (KVL) to the equivalent circuit, we derived the current equations for the transmitting and receiving sides. Assuming high-quality factors for the coils, the currents can be simplified to:
$$ I_F = \left( \frac{M_{TR}}{L_F} \right)^2 \frac{U_T}{R_{EQ}} $$
$$ I_T = \frac{U_T}{\omega L_F} $$
$$ I_R = \frac{M_{TR}}{L_F} \frac{U_T}{R_{EQ}} $$
The output voltage and power are then:
$$ U_R = \frac{M_{TR}}{L_F} U_T $$
$$ P_{OUT} = \left( \frac{M_{TR}}{L_F} \right)^2 \frac{U_T^2}{R_{EQ}} $$
and the efficiency \( \eta \) is calculated as:
$$ \eta = \frac{P_{OUT}}{P_{OUT} + I_F^2 R_F + I_T^2 R_T + I_R^2 R_R} $$
where \( R_F \), \( R_T \), and \( R_R \) are the equivalent resistances of the coils. These equations show that the LCC-S topology offers a constant voltage output, which is independent of the load, making it ideal for electric car charging where battery voltage requirements must be met consistently. This characteristic is particularly advantageous for China EV systems, which may encounter diverse operating conditions.
To design the wireless charging coils, we used Maxwell software simulations to determine the optimal structural parameters. We varied the number of turns and the air gap between coils to analyze their effects on self-inductance and mutual inductance. For instance, with a 6 mm air gap, we tested coils with 5 to 15 turns and recorded the results in a table. The simulations revealed that increasing the number of turns enhances both self-inductance and mutual inductance, up to a point. Based on this, we selected a 15-turn coil for our experimental setup to maximize coupling and power transfer efficiency. The table below summarizes the simulation results, which guided our hardware design for the electric car charging application. This optimization is crucial for ensuring reliable performance in real-world China EV scenarios, where coil alignment and distance can vary.
| Air Gap (mm) | Number of Turns | Self-Inductance \( L_R \) (μH) | Mutual Inductance \( M_{TR} \) (μH) | Self-Inductance \( L_T \) (μH) |
|---|---|---|---|---|
| 6 | 5 | 4.410 | 2.337 | 4.413 |
| 6 | 7 | 7.213 | 4.105 | 7.228 |
| 6 | 9 | 10.092 | 6.019 | 10.091 |
| 6 | 11 | 12.801 | 7.889 | 12.853 |
| 6 | 13 | 15.301 | 9.645 | 15.306 |
| 6 | 15 | 17.319 | 11.117 | 17.360 |
In the experimental validation phase, we constructed the PV-powered wireless charging system for electric cars using the DSP28335 microcontroller as the core controller. The hardware included the PV module, Boost converter, inverter, LCC-S compensation network, and coil assemblies. We first tested the PV system independently to verify MPPT performance. With an input voltage of 10 V, duty cycle of 0.68, load resistance of 45 Ω, and switching frequency of 50 kHz, we measured the output voltage and current. The results showed an output voltage of approximately 30 V, with a slight deviation from the theoretical value due to practical losses in components. The output power was calculated to be 20 W, demonstrating successful MPPT operation under static and dynamic conditions. This is essential for electric car charging, as it ensures maximum energy harvest from solar panels, reducing the overall cost and environmental impact for China EV owners.
For the wireless charging part, we configured the LCC-S topology with the designed coils and compensation components. We applied an AC input voltage and measured the currents and voltages on both the transmitting and receiving sides. The experimental waveforms indicated stable power transfer, with the transmitting current and receiving current in phase, confirming resonance. The measured values included an input voltage \( U_T \) of 30.09 V, input current \( I_F \) of 2.49 A, output voltage \( U_R \) of 15.05 V, and output current \( I_R \) of 4.18 A. The efficiency was computed as approximately 83.96%, which aligns with theoretical predictions. This high efficiency is critical for electric car applications, as it minimizes energy losses during wireless charging, making it a viable option for widespread adoption in the China EV market.
We then integrated the PV and wireless charging systems to form a complete experimental platform. The PV system provided power to the wireless charging module, and we monitored the output power state of the PV array. During tests, we simulated changes in irradiance by adjusting the light source, and the MPPT algorithm quickly adapted to maintain operation at the maximum power point. The waveforms for PV output voltage, current, and load voltage confirmed the system’s responsiveness. Additionally, the currents in the transmitting and receiving coils were observed to be sinusoidal and coupled, indicating effective wireless power transfer to the electric car battery emulator. These experiments validate the practicality of the platform for educational purposes, allowing students to explore the interplay between renewable energy and electric car technologies. The insights gained can drive innovations in China EV infrastructure, supporting the transition to sustainable transportation.
In conclusion, we have successfully developed an experimental platform for photovoltaic-powered wireless charging of electric cars, incorporating MPPT control and LCC-S compensation topology. The system demonstrates efficient energy harvesting from solar panels and reliable wireless power transfer, making it a valuable tool for education and research in power electronics and renewable energy. The use of simulations and hardware experiments provided comprehensive validation, with tables and equations summarizing key aspects. This work highlights the potential of such systems to enhance the China EV ecosystem by offering clean, convenient charging solutions. Future improvements could focus on scalability, cost reduction, and integration with smart grid technologies to further support the growth of electric cars globally.