  The dual active bridge (DAB) converter has been popular in many applications
for its outstanding power density and bidirectional power transfer capacity. Up
to now, triple phase shift (TPS) can be considered as one of the most advanced
modulation techniques for DAB converter. It can widen zero voltage switching
range and improve power efficiency significantly. Currently, current stress of
the DAB converter has been an important performance indicator when TPS
modulation is applied for smaller size and higher efficiency. However, to
minimize the current stress when the DAB converter is under TPS modulation, two
difficulties exist in analysis process and realization process, respectively.
Firstly, three degrees of modulation variables in TPS modulation bring
challenges to the analysis of current stress in different operating modes. This
analysis and deduction process leads to heavy computational burden and also
suffers from low accuracy. Secondly, to realize TPS modulation, if a lookup
table is adopted after the optimization of modulation variables, modulation
performance will be unsatisfactory because of the discrete nature of lookup
table. Therefore, an AI-based TPS modulation (AI-TPSM) strategy is proposed in
this paper. Neural network (NN) and fuzzy inference system (FIS) are utilized
to deal with the two difficulties mentioned above. With the proposed AI-TPSM,
the optimization of TPS modulation for minimized current stress will enjoy high
degree of automation which can relieve engineers' working burden and improve
accuracy. In the end of this paper, the effectiveness of the proposed AI-TPSM
has been experimentally verified with a 1 kW prototype.
