基于遗传算法的多模块IPOP双有源全桥DC-DC变换器总电流有效值优化策略

doi:10.19595/j.cnki.1000-6753.tces.221788

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Abstract The dual active bridge (DAB) converter is one of the most popular DC-DC topologies due to its galvanic isolation between input and output, wide voltage gain, and zero-voltage-switching (ZVS) operation in a wide load range. For a single module dual active bridge (DAB) converter, minimum root mean square current can be achieved using an optimized phase shift control strategy. However, for multi-module parallel operation systems, virtually identical electrical characteristics for each module are difficult to achieve, making the power balance control strategy unable to achieve general optimum efficiency. Therefore, this paper proposes a power difference control strategy for the input parallel and output parallel (IPOP) DAB converter system. By analyzing the difference of auxiliary inductors of each module, a differential power distribution ratio is set, which minimizes the root mean square current of the system.
Firstly, an optimized triple-phase shift control strategy for a single-module DAB converter is analyzed. An optimized triple-phase shift is calculated by introducing a genetic algorithm, which minimizes the root mean square inductor current. Secondly, based on a single-module optimized control strategy, the different RMS inductor current for each module with a differential auxiliary inductor in the IPOP DAB converter system is illustrated. Thirdly, an optimal model to minimize total RMS current for the IPOP DAB converter system is constructed. The optimal results of the model are carried out by a genetic algorithm. Finally, based on the calculation results, an optimal control strategy for the IPOP DAB converter system is proposed. Unlike the traditional strategy that ensure power balancing between modules, the differential power distribution ratio for each module is set based on the difference of auxiliary inductors in the proposed control strategy to minimize the root mean square (RMS) current of the system.
To verify the theoretical analysis, an experimental prototype with three parallel modules based on SIC devices of 8 kW/100 kHz is built. Herein, two conditions are considered: the input voltage of 100 V and output voltage of 80 V, and the input voltage of 100 V and output voltage of 150 V. According to theoretical analysis, when the total load is small, the power distribution ratio for the module with a smaller auxiliary inductor is lower. Moreover, the module with a larger auxiliary inductor distributes larger power. On the contrary, when the total load is large, the module with a larger auxiliary inductor distributes smaller power, and a larger power distribution ratio is set to the module with a smaller auxiliary inductor. The experiment verified the theory of differential power distribution in the whole load range.
Based on theoretical analysis and experimental results, the following conclusions are drawn. (1) The proposed control strategy distributes different power to modules with different auxiliary inductors to meet the total output power requirements, and the system can operate stably under various load conditions. (2) For a single module DAB converter, the optimization strategy can effectively reduce the RMS inductor current. Compared with the traditional control strategy, the optimization effect is more apparent when the load is light and medium, and the complexity of mathematical analysis is reduced. (3) The peak efficiency of the prototype is 98.8 %. The average efficiency is improved by 1.2 % in Buck mode and 0.84 % in Boost mode compared with the traditional power balancing strategy, which verifies the proposed control strategy.

Key words： Dual active bridge converter input parallel output parallel genetic algorithm power distribution inductor root mean square current

Received: 21 September 2022

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TM46

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	Jiang Lingfeng
	Gong Linxiao
	Jin Xinyu
	Chen Jie
	Wang Yong

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Jiang Lingfeng,Gong Linxiao,Jin Xinyu等. Total Root Mean Square Current Optimization of IPOP Dual Active Bridge DC-DC Converter Based on Genetic Algorithm[J]. Transactions of China Electrotechnical Society, 2023, 38(24): 6782-6797.

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