Artificial-Intelligence-Based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter With Full ZVS Range and Optimal Efficiency
Xinze Li, Xin Zhang, Fanfan Lin, Changjiang Sun, Kezhi Mao
Abstract
The 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 the DAB converter. To obtain the desired ZVS and efficiency performance, modulation should be carefully designed. Hybrid modulation (HM) considers several single modulation strategies to achieve good comprehensive performance. Conventionally, to design an HM, a harmonic approach or piecewise approach is used, but they suffer from a 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 the 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. Afterward, 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 a maximum of 97.1% and full-range ZVS operation.