## 1 Introduction
For instance, the authors of [33] investigated efficiency characteristics and optimization techniques under wide DC-link voltage variations, highlighting limited light-load performance. The study detailed in [34] improved light-load performance via bidirectional conduction angle control, but at the expense of losing ZVS for one half-bridge on the secondary side. In [35], the authors proposed a current-stress optimization (CSO) DPS strategy, which reduces current stress but introduces higher system losses in the medium power range compared to EPS, resulting in limited efficiency gains [30; 40]. The approach in [36] optimizes current stress via magnetizing inductance control, which is effectively a topology modification that enables full-load ZVS only in buck mode. In [37], the authors introduced a CSO-DPS strategy that reduces current stress under buck and boost modes, yet with limited ZVS capability. Using EPS modulation, the authors of [38] proposed an optimized power control (OPCB-EPS) strategy to minimize current peaks. While effective in medium power ranges, OPCB-EPS remains suboptimal at low power [30]. For TPS modulation, studies [14; 15; 16; 41; 42; 43; 44; 45; 46; 47; 48; 49] have explored various optimization strategies aimed at minimizing current stress and expanding the ZVS range. Notably, the authorsof [14] proposed a CSO-TPS strategy that provides optimal phase-shift combinations across the full load range, a result corroborated by [15; 16] using different analytical approaches. However, these works primarily focus on current-stress minimization, neglecting the ZVS range and the impact of switching losses on overall efficiency, limiting their effectiveness in efficiency optimization. In [41], the authors proposed a strategy that achieves ZVS for six switches under medium-load conditions, full-switch ZVS at light and heavy loads, and near-optimal peak inductor currents in both buck and boost modes; however, it still leaves two switches without ZVS at medium loads, increasing switching losses. In [42], the authors explored TPS optimization using reinforcement learning (RL) and artificial neural networks (ANN), though RL systems face challenges for real-time industrial deployment. In [43], the authors employed artificial intelligence (AI) techniques to build current-stress models from simulation data and applied particle swarm optimization (PSO) and fuzzy inference systems (FIS) to learn and store optimal modulation results. However, this approach relies on extensive simulation or experimental data and requires optimizing each operating condition separately, resulting in a complex and time-consuming process. In [44], the authors creatively proposed a reactive power controller based on triple phase shift, which significantly reduces the inductor current under all load conditions by optimizing the reactive power. However, the ZVS performance over the full operating range has not been further explored. In [45], the authors perform nonlinear modeling of the switching process, improving ZVS performance and achieving high efficiency across the full load range. However, the method is implemented based on lookup tables, preventing real-time computation and limiting portability. In [46], the author proposed an optimal primary-side duty cycle modulation scheme to reduce current stress; however, its performance under light-load conditions still has room for improvement [47]. Moreover, although the ZVS range is extended compared to SPS, full-switch ZVS cannot be achieved under high voltage conversion ratios and elevated power transfer conditions. In [48], the authors explored the intrinsic correlation between current stress and RMS optimization under TPS control and proposed an optimization strategy; however, ZVS analysis was omitted. Additionally, the switching frequency was limited to 5 kHz, leaving the effectiveness under high-frequency operation unverified. In [49], a triple phase shift-based approach was proposed to minimize current stress under varying DC-link voltages.