Research on parallel control strategy of power converters based on fuzzy neural network

As pure electric vehicles shift toward intelligent technology, the energy demand for onboard equipment is on the rise. In this study, a parallel control strategy for two 3-kW DC-DC power converters was proposed to meet the power requirements of pure electric vehicle loads in this paper. First, the operation mode of the resonant power converter was analyzed. The operation mode of the power converter adopted the advantageous frequency conversion-phase shift control mode. Second, a parallel control method for Takagi-Sugeno-type fuzzy neural network converters with four inputs and a single output first-order was designed to meet the power demand based on the advantages of fuzzy control and neural networks. The neural networks can be trained automatically based on the established requirements, and the fuzzy rules formulated through fuzzy neural networks were more detailed and accurate. Finally, the proposed control strategy was validated by experiments. The experimental results showed that the proposed control strategy can ensure the stable operation of the power converter during switching under the set load. The output power of the primary and sub converters varies linearly, which can meet the load's demand for high power. There is no need to develop higher-power power converters. These results can provide a new idea for the research of high-power power converters and reduce development costs.