In recent years, the rapid adoption of electric vehicles (EVs) has highlighted the need for sustainable and efficient charging solutions. As a researcher in the field of power electronics and renewable energy systems, I have focused on integrating photovoltaic (PV) technology with wireless power transfer (WPT) to create an experimental platform for electric vehicle charging. This platform aims to demonstrate how solar energy can be harnessed to power EVs in China, addressing the growing demand for clean transportation. The system combines PV generation, maximum power point tracking (MPPT), and inductive coupling for wireless charging, providing a hands-on educational tool for students to explore key concepts in energy conversion. In this article, I will detail the design, simulation, and experimental validation of this platform, emphasizing its relevance to the China EV market and the broader context of renewable energy integration.
The experimental platform centers on a DSP28335 microcontroller as the core controller, which manages both the PV side and the wireless charging side. The PV system converts solar energy into electrical power, while the WPT system transmits this power wirelessly to an electric vehicle battery. This setup not only showcases practical applications but also serves as a teaching aid for understanding complex topics like MPPT algorithms, resonant topologies, and electromagnetic design. By using simulations in tools like Simulink and Maxwell, we have optimized the system components, and through hardware implementation, we have verified its performance. The integration of these elements makes this platform ideal for educational purposes, allowing students to gain insights into the output characteristics of PV cells and the operational mechanisms of wireless energy transfer for electric vehicles.
The PV power generation system forms the foundation of this experimental platform. It consists of a PV module, a DC-DC converter (specifically a Boost circuit), and a PWM driver module with MPPT control. The Boost circuit is chosen for its ability to step up the voltage from the PV panel to a level suitable for the wireless charging system. The operating principle of the Boost circuit can be described by the following equations based on inductor volt-second balance. For a switching period T, with on-time T_on and off-time T_off, the output voltage U_o relates to the input voltage U_i and duty cycle D as follows:
$$ U_o = \frac{U_i}{1 – D} $$
Assuming ideal conditions where input power equals output power, we have U_PV I_PV = U_R I_R, where U_PV and I_PV are the PV output voltage and current, and U_R and I_R are the load voltage and current. The equivalent resistance R_eq seen by the PV panel can be derived as:
$$ R_{eq} = (1 – D)^2 R $$
where R is the load resistance. This relationship shows that by adjusting the duty cycle D of the PWM signal, we can vary the equivalent resistance, thereby controlling the PV output to track the maximum power point (MPP). This is crucial for maximizing energy harvest from the PV panel, especially in the context of electric vehicles where efficient charging is paramount in China’s evolving EV infrastructure.
For MPPT, we implemented the perturb and observe (P&O) method, which is widely used due to its simplicity and effectiveness. The 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. This iterative process ensures that the system operates near the MPP under varying environmental conditions. The flowchart of the P&O algorithm illustrates this logic: start by measuring voltage and current, calculate power, compare with previous power, and adjust the duty cycle accordingly. This method is particularly beneficial for electric vehicle applications in China, where solar irradiance and temperature can fluctuate, affecting PV performance.
To validate the PV system design, we built a simulation model in Simulink. The model includes a PV array, Boost converter, and MPPT controller. Under different conditions, such as changes in irradiance and temperature, the simulation results demonstrate effective MPPT tracking. For instance, at an irradiance of G = 1000 W/m², when temperature abruptly increases from 25°C to 45°C at 0.2 s and then to 60°C at 0.4 s, the system quickly adjusts to the new MPP. Similarly, at a constant temperature of T = 25°C, when irradiance jumps from 1000 W/m² to 1250 W/m² at 0.2 s and to 1500 W/m² at 0.4 s, the MPP is tracked efficiently. These simulations confirm that the PV model can rapidly adapt to environmental changes, which is essential for reliable operation in real-world electric vehicle charging scenarios in China.
The wireless charging system employs an LCC-S compensation topology, which is known for its constant voltage output characteristics, making it suitable for electric vehicle batteries. The circuit comprises an inverter, transmitting and receiving coils with compensation capacitors, a rectifier, and a load representing the EV battery. The resonant frequency ω is set to ensure efficient power transfer. The compensation components are designed to satisfy the resonance condition:
$$ L_F C_F = L_R C_R = (L_T – L_F) C_T = \frac{1}{\omega^2} $$
where L_F and C_F form the filter on the transmitting side, L_T and C_T are the transmitting coil inductance and compensation capacitor, and L_R and C_R are the receiving coil inductance and compensation capacitor. Using fundamental harmonic analysis, the equivalent AC resistance R_EQ, input voltage U_T, and output voltage U_R are given by:
$$ R_{EQ} = \frac{8}{\pi^2} R_L $$
$$ U_T = \frac{2\sqrt{2}}{\pi} U_{INV} $$
$$ U_R = \frac{2\sqrt{2}}{\pi} U_{REC} $$
Applying Kirchhoff’s voltage law to the equivalent circuit model, we derive the current equations for the transmitting and receiving sides. Neglecting coil resistances for high-Q designs, the currents I_T and I_R can be expressed as:
$$ I_F = \left( \frac{M_{TR}}{L_F} \right)^2 \frac{U_T}{R_{EQ}} $$
$$ I_T = \frac{U_T}{\omega L_F} $$
$$ I_R = \frac{M_{TR}}{L_F} \frac{U_T}{R_{EQ}} $$
where M_TR is the mutual inductance between the transmitting and receiving coils. The output voltage and power are then:
$$ U_R = \frac{M_{TR}}{L_F} U_T $$
$$ P_{OUT} = \left( \frac{M_{TR}}{L_F} \right)^2 \frac{U_T^2}{R_{EQ}} $$
The efficiency η of the system is calculated as the ratio of output power to total input power, accounting for losses in the coil resistances:
$$ \eta = \frac{P_{OUT}}{P_{OUT} + I_F^2 R_F + I_T^2 R_T + I_R^2 R_R} $$
These equations highlight that the LCC-S topology provides a constant output voltage independent of the load, which is advantageous for charging electric vehicle batteries consistently. This feature is particularly relevant for the China EV market, where standardization and reliability are key concerns.
For the coil design, we used Maxwell software to simulate different coil configurations and determine the optimal structural parameters. The coils were designed as planar structures with specific dimensions, and simulations were conducted at a frequency of 85 kHz with a 6 mm air gap. The results for various turn numbers are summarized in the table below, showing how self-inductance and mutual inductance vary with coil turns. This data informed our selection of a 15-turn coil for the experimental setup, as it offered higher inductance values suitable for efficient power transfer in electric vehicle applications.
| Air Gap (mm) | Coil 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 cases, we constructed the PV system using a Boost circuit with components including inductors, capacitors, and MOSFET switches. The hardware was tested under conditions such as an input voltage U_g = 10 V, duty cycle D = 0.68, load resistance R = 45 Ω, and switching frequency f_s = 50 kHz. Waveforms for switch voltage u_s, input current i_in, and output voltage u_o were measured, showing that the output voltage reached approximately 30 V with a calculated output power of 20 W. Minor deviations from theoretical values were attributed to practical losses, but overall, the Boost circuit performed reliably, demonstrating the effectiveness of the MPPT control for electric vehicle charging systems.
For the wireless charging part, we built the LCC-S topology and conducted experiments to measure input and output currents and voltages. The results showed that with an input voltage U_T = 30.09 V and input current I_F = 2.49 A, the output voltage U_R was 15.05 V and output current I_R was 4.18 A. The efficiency was computed as:
$$ \eta = \frac{15.05 \times 4.18}{30.09 \times 2.49} \approx 0.8396 $$
indicating that over 83% of the power was transferred efficiently. This confirms the feasibility of the wireless system for electric vehicles, particularly in the context of China’s push for advanced EV technologies. Additionally, when integrating the PV and wireless systems, we observed that the MPPT algorithm quickly tracked the maximum power point after changes in irradiance, and the wireless module successfully transmitted power to the load, as evidenced by current waveforms in the coils.
Through this experimental platform, students can engage with real-world challenges in renewable energy and electric vehicle charging. The hands-on experience with circuit design, simulation, and testing helps deepen their understanding of PV output characteristics and wireless power transfer mechanisms. As the demand for electric vehicles in China continues to grow, such educational tools are invaluable for training the next generation of engineers in sustainable technologies. The platform not only validates theoretical concepts but also encourages innovation in integrating solar power with EV infrastructure, contributing to the broader goals of energy efficiency and environmental sustainability.
In conclusion, the development of this photovoltaic-powered wireless charging experimental platform has proven successful in demonstrating key principles of energy conversion and transfer. The use of MPPT algorithms and LCC-S topologies ensures efficient operation, while simulations and hardware experiments provide comprehensive learning opportunities. This work underscores the importance of renewable energy integration in the electric vehicle sector, particularly in China, where EV adoption is accelerating. Future enhancements could focus on scaling the system for higher power applications or incorporating advanced control strategies to further improve performance. Overall, this platform serves as a robust foundation for educational and research initiatives aimed at advancing clean transportation solutions.