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.

翻译：双有源桥（DAB）变换器是无线充电、电动汽车和可再生能源等众多重要应用中的关键器件。零电压开关（ZVS）范围与效率是DAB变换器的两项重要性能指标。为实现理想的ZVS与效率性能，需精心设计调制策略。混合调制通过整合多种单一调制策略以获取优异的综合性能。传统方法采用谐波分析法或分段分析法设计混合调制，但存在建模过程耗时且精度不足的问题。为此，本文提出一种基于人工智能的混合扩展移相（HEPS）调制方案。该HEPS调制采用自动化开发方式，在保持高模型精度的同时，显著简化了繁琐的建模流程。HEPS调制中融合两种扩展移相（EPS）策略，可在全工作范围内实现全ZVS工况下的最优效率。具体实施时，采用当前先进的集成学习算法——极端梯度提升（XGBoost）构建ZVS与效率性能的数据驱动模型，进而利用基于状态自适应速度限制的粒子群优化（PSO-SAVL）算法选择最优EPS策略并优化调制参数。通过1 kW硬件实验验证，HEPS方案实现了最高97.1%的效率与全范围ZVS运行，充分证明了其可行性。