Efficiency Breakthrough in Wireless Charging for Electric Trucks
In the rapidly evolving world of electric mobility, where every percentage point in energy efficiency can translate into extended range and reduced operating costs, a new advancement in wireless charging technology is capturing the attention of engineers and industry leaders alike. A team of researchers from Sevastopol State University in Russia, in collaboration with scholars from T.F. Gorbachev Kuzbass State Technical University and Al-Azhar University in Egypt, has introduced a refined optimization method for wireless charging systems that significantly enhances their efficiency while maintaining practical design constraints. Their findings, published in the March 2024 issue of the Transactions of China Electrotechnical Society, offer a promising pathway toward more reliable and high-performance charging infrastructure for heavy-duty electric vehicles.
The study, led by Valery Zavyalov, Irina Semykina, Evgeny Dubkov, Amet-Khan Velilyaev, and Amr Refky, focuses on wireless power transfer (WPT) systems utilizing an LC-series compensating topology. This configuration, known for its robust power transfer ratio and minimal sensitivity to changes in coil distance, is particularly well-suited for applications involving electric trucks, where alignment precision may vary during operation. The research team’s objective was not merely to boost efficiency in ideal conditions, but to achieve constrained optimization—balancing high efficiency with real-world limitations such as physical space, component voltage ratings, and required power output.
At the heart of their approach lies a mathematical model that simplifies the complex dynamics of resonant circuits into a manageable framework for optimization. While many previous studies have relied on computationally intensive three-dimensional finite-element methods to simulate electromagnetic fields, the authors opted for a more practical equivalent circuit model. This choice reflects a strategic balance between accuracy and feasibility, enabling engineers to perform rapid design iterations without requiring supercomputing resources. The model assumes an idealized inverter and rectifier, active resistive load, and neglects non-ohmic losses in initial calculations—a simplification that allows the core relationships between circuit parameters and system performance to be clearly identified.
The optimization criteria are structured around four key functions. The primary goal is to maximize efficiency, defined as the ratio of power delivered to the load versus power drawn from the input source. However, this is not pursued in isolation. The team also imposes lower limits on the amount of power transferred, ensuring the system meets operational demands, and upper limits on the voltages across the primary and secondary capacitors, safeguarding component reliability. These constraints are critical in industrial applications where overvoltage can lead to premature failure of expensive electronic components.
What sets this research apart is its systematic method for reducing complexity. With numerous interdependent variables—such as inductances, capacitances, resistances, and mutual inductance—the direct analytical solution of the optimization problem becomes intractable. To overcome this, the researchers leveraged the physical and geometric constraints inherent in the application. For the electric truck ET−20132, the receiving coil dimensions are fixed at 600 mm by 300 mm due to vehicle design limitations. The distance between the transmitting and receiving coils is also held constant at 100 mm, a realistic gap for a ground-based charging pad and an undercarriage-mounted receiver.
Given these fixed parameters, the number of coil turns emerges as the primary design variable. As the number of turns increases, so do the self-inductance and resistance of the coils, while mutual inductance also rises due to enhanced magnetic coupling. Capacitance, in turn, is determined by the requirement to maintain a resonant frequency of approximately 90 kHz, as recommended by the SAE J2954 standard for light-duty electric vehicles. This linkage allows the entire system to be parameterized by a single variable: the coil inductance.
To establish precise mathematical relationships between inductance and other circuit parameters, the team employed Chebyshev polynomial approximation using the least-squares method. This numerical technique enabled them to derive high-accuracy expressions for resistance, mutual inductance, and inverse capacitance as functions of inductance. The resulting third-order polynomials achieved root-mean-square errors of less than 2% across all parameters, ensuring that the simplified model remains faithful to the underlying physics.
With these approximations in place, the optimization criteria could be evaluated as functions of inductance alone. The analysis revealed that efficiency peaks at a coil inductance of 22.5 microhenries. However, since the number of turns must be an integer, the closest feasible solution corresponds to five turns, yielding an inductance of 24 microhenries. At this point, all constraints are satisfied: the system delivers more than the required 3.6 kW of power, capacitor voltages remain below the 900-volt safety threshold, and efficiency is maximized within the allowable design space.
To validate their theoretical findings, the researchers constructed a full-scale prototype tailored to the ET−20132 electric truck. The system features flat, square-shaped coils with five turns each, a high-frequency inverter using IRFP90N20DPbF MOSFET transistors, and a high-voltage bridge rectifier with STPS160H100TV Schottky diodes. Metal-film capacitors from EPCOS (B32682A1472K000) were used on both primary and secondary sides, and measurements were conducted using precision instruments including a DELTA ELEKTRONIKA SM 330-AR-22 DC power supply, OWON SDS7102V oscilloscope, and MASTECH MS2109A current clamp.
Initial measurements of the physical system revealed minor deviations from the theoretical model—approximately 2% on average—with slight asymmetry between primary and secondary components. These discrepancies are expected in real-world implementations due to manufacturing tolerances and material inconsistencies. More significantly, the actual resonant frequency was measured at 91.3 kHz, slightly higher than the target 90 kHz. This shift is attributed to the combined effect of parameter variations and circuit asymmetry, underscoring the importance of post-design calibration in practical deployments.
To assess system efficiency accurately, the team accounted for losses not included in the initial mathematical model. These include skin effect losses in the copper windings, switching and conduction losses in the MOSFETs and diodes, and power consumed by control circuitry. The skin effect, which causes current to concentrate near the surface of conductors at high frequencies, was measured using an RLC meter and found to increase coil resistance by 0.16 ohms at resonance. Transistor losses were estimated based on the on-state resistance of the MOSFETs (0.023 ohms per device), while diode losses were calculated using a forward voltage drop of 0.68 volts per diode.
After incorporating these corrections, the experimental data showed strong alignment with the refined theoretical model, particularly around the resonant frequency. Load current measurements matched the predicted values closely within the operating range of 91.3 kHz to 92.5 kHz, confirming that the system consistently delivers the required power. Capacitor voltage readings, while slightly higher than modeled, remained within safe limits and were attributed to parasitic capacitances introduced by measurement equipment.
The most notable result was the system’s efficiency. Direct measurement of input and output power yielded a peak efficiency of 82.9% at 92.3 kHz. However, when corrected for auxiliary losses—including 4 watts from the transmitter control circuit and 2 watts from the receiver control circuit—the efficiency rose to 94.8% at 98.1 kHz. Given that high-power wireless charging systems are typically rated based on inductive power transfer efficiency alone, this corrected figure is the most relevant benchmark. Within the intended operating range of 91.3–92.5 kHz, the average corrected efficiency was 91%, a figure that compares favorably with current commercial systems.
The researchers emphasize that this 91% efficiency is achieved under realistic conditions, accounting for all major loss mechanisms. In an idealized scenario where skin effect and semiconductor losses are eliminated, the model predicts a maximum efficiency of 99.2%. While such perfection is unattainable in practice, the gap between theoretical and experimental results highlights the potential for further improvement through advanced materials, better thermal management, and optimized control strategies.
One of the key advantages of the LC-series topology, as demonstrated in this study, is its stability. Unlike other compensation schemes, its resonant frequency is minimally affected by changes in mutual inductance, which varies with coil alignment and distance. This insensitivity ensures that the system maintains high efficiency even when the vehicle is not perfectly positioned over the charging pad—a common occurrence in real-world operations. For fleet operators, this translates into greater operational flexibility and reduced need for precise parking guidance systems.
The implications of this research extend beyond the specific application to the ET−20132 truck. The proposed optimization framework can be adapted to other vehicle classes and power levels, provided that geometric and operational constraints are clearly defined. The use of polynomial approximation to reduce multidimensional design spaces into single-variable problems offers a scalable methodology that can be integrated into automated design tools. For automotive engineers, this means faster development cycles and more reliable performance predictions.
Moreover, the success of this constrained optimization approach reinforces the value of physics-based modeling in an era increasingly dominated by data-driven techniques. While machine learning and artificial intelligence have their place in control and diagnostics, fundamental electromagnetic principles remain essential for achieving breakthroughs in hardware design. The ability to derive actionable insights from first principles—rather than relying solely on simulation or trial-and-error experimentation—is a hallmark of rigorous engineering research.
From a sustainability perspective, improving wireless charging efficiency directly contributes to reducing the overall energy footprint of electric transportation. Higher efficiency means less energy is wasted as heat, which in turn reduces cooling requirements, extends component lifespans, and lowers electricity consumption. For commercial fleets, where vehicles may charge multiple times per day, even a few percentage points in efficiency gain can result in significant cost savings over time.
The research also highlights the importance of international collaboration in advancing clean energy technologies. The partnership between Russian and Egyptian institutions demonstrates how shared scientific goals can bridge geographical and cultural divides. Such collaborations foster innovation by combining diverse expertise and perspectives, accelerating the pace of technological progress.
Looking ahead, the team suggests several directions for future work. One is the integration of adaptive control strategies that can dynamically adjust operating frequency or compensation parameters in response to changing conditions. Another is the exploration of alternative coil geometries and magnetic core materials to further enhance coupling and reduce losses. Additionally, extending the model to include temperature-dependent effects could improve long-term reliability predictions.
In conclusion, the work by Zavyalov, Semykina, Dubkov, Velilyaev, and Refky represents a significant step forward in the practical realization of efficient wireless charging for electric vehicles. By combining rigorous mathematical analysis with experimental validation, they have demonstrated that high efficiency—on the order of 91%—is achievable in real-world systems when design is guided by a systematic optimization framework. Their approach balances theoretical elegance with engineering pragmatism, offering a blueprint for the next generation of wireless charging infrastructure.
As cities and industries continue to electrify their transportation fleets, innovations like this will play a crucial role in making electric mobility not only viable but also economically and environmentally sustainable. The road to a zero-emission future is paved not just with batteries and motors, but also with smart, efficient, and reliable charging solutions that keep those vehicles moving.
Valery Zavyalov, Irina Semykina, Evgeny Dubkov, Amet-Khan Velilyaev, Amr Refky, Sevastopol State University, Transactions of China Electrotechnical Society, DOI: 10.19595/j.cnki.1000-6753.tces.222371