  Dual active bridge (DAB) converter is the key enabler in many popular
applications such as wireless charging, electric vehicle and renewable energy.
ZVS range and efficiency are two significant performance indicators for DAB
converter. To obtain the desired ZVS and efficiency performance, modulation
should be carefully designed. Hybrid modulation considers several single
modulation strategies to achieve good comprehensive performance.
Conventionally, to design a hybrid modulation, harmonic approach or piecewise
approach is used, but they suffer from time-consuming model building process
and inaccuracy. Therefore, an artificial-intelligence-based hybrid extended
phase shift (HEPS) modulation is proposed. Generally, the HEPS modulation is
developed in an automated fashion, which alleviates cumbersome model building
process while keeping high model accuracy. In HEPS modulation, two EPS
strategies are considered to realize optimal efficiency with full ZVS operation
over entire operating ranges. Specifically, to build data-driven models of ZVS
and efficiency performance, extreme gradient boosting (XGBoost), which is a
state-of-the-art ensemble learning algorithm, is adopted. Afterwards, particle
swarm optimization with state-based adaptive velocity limit (PSO-SAVL) is
utilized to select the best EPS strategy and optimize modulation parameters.
With 1 kW hardware experiments, the feasibility of HEPS has been verified,
achieving optimal efficiency with maximum of 97.1% and full-range ZVS
operation.
