Optimized Control Strategy Enhances Efficiency of Three-Port DC-DC Converters in EVs and Renewable Systems
As the global push toward electrification accelerates, the integration of electric vehicles (EVs), renewable energy sources, and energy storage systems into smart grids has become a central focus of modern power electronics research. Among the most critical components enabling this integration are advanced DC-DC converters, which serve as the backbone for efficient power transfer across diverse energy sources and loads. A recent breakthrough in this domain comes from a team of researchers at Hunan University of Technology and the National Electric Power Conversion and Control Engineering Technology Research Center at Hunan University. Their work, published in the Transactions of China Electrotechnical Society, introduces a novel control strategy that significantly improves the efficiency of triple active bridge (TAB) DC-DC converters—devices increasingly vital in next-generation EV powertrains and renewable microgrids.
The research, led by Lan Zheng, Wang Xueli, Yu Xueping, Zou Bin, and Liu Bei, addresses a long-standing challenge in high-efficiency power conversion: minimizing current stress and conduction losses in multi-port systems. TAB converters are particularly promising due to their ability to integrate multiple energy sources—such as batteries, supercapacitors, and photovoltaic arrays—into a single, compact power interface. This eliminates the need for multiple standalone converters, thereby reducing system complexity, footprint, and cost. However, achieving high efficiency across varying operating conditions has proven difficult, primarily due to elevated root mean square (RMS) currents in the magnetic components, which directly impact conduction losses and thermal management.
Traditional control methods, such as single phase shift (SPS), offer simplicity but suffer from limited control flexibility and high reactive power circulation. This results in increased RMS current, reduced efficiency, and higher thermal stress on semiconductor devices. To overcome these limitations, more sophisticated control schemes like phase shift plus pulse width modulation (PS-PWM) have been explored. While PS-PWM provides greater degrees of freedom for optimizing power flow and minimizing losses, it introduces significant mathematical complexity, making real-time implementation and analytical modeling challenging.
The team’s innovation lies in a hybrid approach that combines circuit decomposition theory with intelligent optimization algorithms. By decomposing the complex PS-PWM-controlled TAB system into simpler, analytically tractable sub-circuits, the researchers were able to derive unified expressions for port power and inductor current RMS values under various operating modes. This decomposition not only simplifies the modeling process but also enables a more intuitive understanding of the internal power dynamics within the converter.
At the heart of their methodology is the recognition that the total conduction loss in a TAB system is proportional to the sum of the squares of the inductor current RMS values across all ports. This includes losses in the switching devices (MOSFETs or IGBTs) and the copper windings of the high-frequency transformers. By formulating an optimization problem that minimizes this sum, the researchers established a direct link between control parameters and system efficiency.
However, solving this optimization problem analytically is impractical due to the nonlinearity and multi-variable nature of the constraints. Instead of relying on traditional numerical methods, the team employed a genetic algorithm (GA)—a bio-inspired optimization technique that mimics natural selection and evolution. This approach allows for a global search of the solution space, avoiding local minima that often trap gradient-based solvers.
The genetic algorithm was configured with a population size of 100, a maximum of 50 generations, a crossover probability of 0.6, and a mutation rate of 0.01. These parameters were carefully selected to balance computational efficiency and solution accuracy. The algorithm iteratively evolved a population of candidate solutions (represented as combinations of phase shift angles and duty ratios), evaluating each based on a fitness function tied to the RMS current sum. Over successive generations, the population converged toward optimal control parameters that minimized losses across a wide range of power delivery scenarios.
One of the most significant contributions of this work is the development of a pre-computed lookup table strategy. Rather than performing real-time optimization—a computationally intensive task unsuitable for embedded controllers—the researchers used the genetic algorithm to generate optimal control parameters offline. These parameters were then stored in a lookup table indexed by operating conditions such as port power levels and voltage matching ratios. During actual system operation, the controller simply retrieves the pre-optimized settings based on real-time measurements, enabling fast, efficient, and loss-minimized control without the burden of online computation.
This approach aligns well with practical implementation requirements in automotive and industrial applications, where deterministic response times and reliability are paramount. For instance, in an electric vehicle, the TAB converter might interface a high-voltage battery, a low-voltage auxiliary system, and a regenerative braking circuit. The ability to dynamically adjust control parameters based on driving conditions—such as acceleration, cruising, or charging—ensures that the system operates at peak efficiency across all modes.
To validate their strategy, the team conducted extensive simulations using MATLAB/Simulink, followed by experimental testing on an RT-Lab real-time simulation platform. The experimental setup included a full-scale TAB prototype with a 120 V input port, a 240 V output port, and a third port configurable for energy storage. The power inductors and transformer were carefully designed to match the simulation parameters, ensuring consistency between virtual and physical testing.
Simulation results demonstrated a consistent reduction in the sum of squared RMS currents under the proposed Genetic Algorithm Optimal Strategy (GAOS) compared to conventional SPS control. For example, at a voltage matching ratio (k21) of 1.2, with port 2 delivering 0.4 per unit (pu) power and port 3 delivering 0.2 pu, the GAOS achieved an RMS current sum of 0.777, compared to 0.803 under SPS—a 3.2% improvement. Similar gains were observed across different voltage ratios and load conditions, confirming the robustness of the approach.
Experimental validation further corroborated the simulation findings. In one test case with k21 = 1.4, port 2 delivering 840 W, and port 3 delivering 288 W, the GAOS reduced the sum of squared RMS currents from 0.6 (SPS) to 0.428—a remarkable 28.7% reduction. This translated directly into lower conduction losses and improved overall system efficiency. Thermal imaging of the converter during operation showed visibly lower temperature rise in the MOSFETs and transformer under GAOS, indicating reduced thermal stress and longer component lifespan.
The researchers also evaluated the dynamic performance of the system under load transients. When the load on port 2 was abruptly changed, the output voltage on port 3 remained stable at 240 V, and the current adjusted smoothly to maintain constant power. Similarly, when port 3 experienced a load step, port 2 maintained its output voltage and current within tight tolerances. This demonstrates the system’s ability to decouple power flows between ports, a critical feature for maintaining stability in multi-source, multi-load environments.
Another key advantage of the proposed method is its adaptability to varying voltage levels. In real-world applications, the voltage of energy storage systems—such as lithium-ion batteries—can vary significantly over their state of charge. The GAOS strategy, by incorporating voltage matching ratios into the optimization framework, automatically adjusts control parameters to maintain efficiency across the entire operating range. This is particularly beneficial in hybrid energy storage systems where batteries and supercapacitors operate at different voltage levels.
From a system design perspective, the reduction in RMS current also allows for the use of smaller magnetics and lower-rated semiconductors, further reducing cost and volume. In electric vehicles, where space and weight are at a premium, such improvements can directly contribute to increased range and reduced manufacturing costs.
The implications of this research extend beyond automotive applications. In renewable energy microgrids, where solar panels, wind turbines, and battery banks must be efficiently integrated, the TAB converter with GAOS control can serve as a central power management hub. Its ability to minimize losses while maintaining high power quality makes it ideal for distributed energy systems, especially in off-grid or islanded configurations where every watt of efficiency counts.
Moreover, the methodology is not limited to three-port systems. The circuit decomposition and genetic algorithm framework can be extended to four-port or even higher-order multi-active bridge converters, opening new avenues for ultra-efficient, multi-functional power conversion architectures.
The work also highlights the growing importance of intelligent control in power electronics. As systems become more complex and performance demands increase, traditional control strategies are reaching their limits. Machine learning, evolutionary algorithms, and other AI-driven techniques are emerging as essential tools for unlocking the full potential of power electronic systems.
In conclusion, the research by Lan Zheng and colleagues represents a significant step forward in the design and control of multi-port DC-DC converters. By combining circuit theory with advanced optimization techniques, they have developed a practical, high-performance solution that enhances efficiency, reduces losses, and improves system reliability. Their approach not only advances the state of the art in power electronics but also provides a blueprint for future innovations in smart energy systems.
As the world transitions to a more electrified and sustainable future, technologies like the optimized TAB converter will play a crucial role in enabling cleaner, more efficient, and more resilient energy infrastructure. Whether in the next generation of electric vehicles or in community-scale renewable microgrids, the impact of this research is likely to be felt for years to come.
Lan Zheng, Wang Xueli, Yu Xueping, Zou Bin, Liu Bei, Hunan University of Technology and Hunan University, Transactions of China Electrotechnical Society, DOI: 10.19595/j.cnki.1000-6753.tces.231541