A facile and environmentally friendly fabrication is proposed to prepare nitrogen-doped hierarchical porous activated carbon via normal-pressure popping, one-pot activation and nitrogen-doping process. The method adopts paddy as carbon precursor, KHCO₃ and dicyandiamide as the safe activating agent and nitrogen dopant. The as-prepared activated carbon presents a large specific surface area of 3025 m²·g⁻¹ resulting from the synergistic effect of KHCO₃ and dicyandiamide. As an electrode material, it shows a maximum specific capacitance of 417 F·g⁻¹ at a current density of 1 A·g⁻¹ and very good rate performance. Furthermore, the assembled symmetric supercapacitor presents a large specific capacitance of 314.6 F·g⁻¹ and a high energy density of 15.7 Wh·Kg⁻¹ at 1 A·g⁻¹, maintaining 14.4 Wh·Kg⁻¹ even at 20 A·g⁻¹ with the energy density retention of 91.7%. This research demonstrates that nitrogen-doped hierarchical porous activated carbon derived from paddy has a significant potential for developing a high-performance renewable supercapacitor and provides a new route for economical and large-scale production in supercapacitor application.

In the three-level (3L) neutral-point-clamped (NPC) dual-active-bridge (DAB) converter, both sides of the converter can adapt to higher dc bus voltage, and more control degrees of freedom (DoFs) are provided compared to the conventional DAB converter. In the pulse width modulation (PWM) generating technique with five DoFs, loss imbalance may develop in inner switches and outer switches because the leading or lagging arm is introduced, affecting system reliability. In addition, the transient dc-bias issue may also arise in the 3L-NPC-DAB converter modulated with five DoFs, leading to the transformer saturation. In this article, two strategies are proposed to suppress the loss imbalance and the transient dc-bias in the current stress and magnetizing branches. It is also general and applicable for single-phase-shift, dual-phase-shift, and triple-phase-shift modulations. Furthermore, the loss balance strategy and dc-bias suppression strategy are applied in the PWM generation technique simultaneously without additional components. With the proposed solutions, the loss imbalances of inner switches and outer switches are suppressed and dc-bias in the integral of voltages on both sides of the 3L-NPC-DAB converter is abolished. The effectiveness of the proposed strategies is validated by a series of experiments conducted under varying situations.

The three-level neutral-point-clamped dual-active-bridge (3L-NPC-DAB) converter offers several advantages over the conventional two-level DAB converter, including enhanced control flexibility and the ability to operate with lower voltage-rated power switches. However, with the increase in the number of power devices and multiobjective control requirements, traditional phase shift control cannot meet the requirements. In order to further improve the conversion efficiency, this study introduces a deep reinforcement learning (DRL) optimization scheme based on the five control degrees of freedom (5-DoF) technique. The proposed scheme utilizes the deep deterministic policy gradient (DDPG) algorithm to minimize power losses and determine optimal control solutions. Through training, the DDPG agent acts as a predictor, enabling effective control decisions for maximizing conversion efficiency across various operating conditions. The effectiveness of the proposed method is validated through experimental results obtained from a laboratory prototype.

In order to enhance the conversion efficiency of dual-active-bridge converters, a hybrid five-variable (HFV) modulation strategy with the aid of deep reinforcement learning technique is proposed in this article. Specifically, deep deterministic policy gradient (DDPG) algorithm is employed to train the agent with the purpose of the lower root mean square (RMS) current and the zero voltage switching (ZVS) operation of all power switches under various conditions. Moreover, due to the complexity of HFV modulation with five control DoF, frequency-domain analysis is used to directly derive the operating expressions of the converter. Based on these, the trained DDPG agent can output the optimal values of control variables, which can lower the RMS current and achieve the widest ZVS operation with reduced computation burden. A thorough comparison among related works in terms of the RMS current and ZVS range was presented. Finally, the effectiveness of the proposed method is verified by experiments on a built prototype.

As one of promising power interfaces in Microgrids, dual active bridge (DAB) converters have been extensively studied and widely applied. However, dc-bias current during its transient process, as well as the electromagnetic interference and the reduced efficiency caused by the hard-switching operation, urgently need to be addressed. In addition, traditional transient optimization strategies mainly focus on unidirectional power transmission, which cannot ensure fast power reverse operation of DAB converters and eliminate dc-bias current and hard-switching defects at the same time. To address this issue, a novel transient power reversal control (TPRC) strategy based on the principle of straightforward phase-shift optimization is proposed in this article. Extensive and comparative experimental evaluations under various scenarios have been conducted to verify the effectiveness of the proposed TPRC strategy.

The conversion efficiency of dual active bridge (DAB) converter could be improved by increasing the number of control degrees of freedom (DoF). A modulation with six control degrees of freedom (6-DoF) is proposed in this article to further improve the efficiency of DAB converters. First, frequency-domain analysis is used to directly derive the operating expressions of the converter, including the transmission power, inductor current, and root-mean-square (RMS) current. Then, a deep reinforcement learning (DRL) optimization scheme with 6-DoF (DRL-6DoF) is proposed. Specifically, the deep deterministic policy gradient (DDPG) algorithm is used for solving optimal solutions with the minimum power losses. The trained DDPG agent can output the optimal values of control variables under various operation conditions. With the proposed DRL-6DoF control scheme, the efficiency of the DAB converter is further improved because of the low RMS current and great ZVS performance. Especially under heavy load conditions, the efficiency is great improved because of the characteristic of variable switching frequency. Finally, the effectiveness of the proposed method is verified by experimental results on a built prototype.