This paper proposes a bi-objective dispatch framework for multi-energy systems (MES) to balance operating cost and carbon emissions in coupled electricity and district heating networks. The model coordinates heating-network and building thermal inertia with aggregated electric vehicle charging stations, which are represented as battery energy storage systems with vehicle-to-grid capability. The ε-constraint method is used to generate the Pareto frontier, and LINMAP is adopted to select the compromise solution. To interpret carbon-abatement effects, three sensitivity metrics, GACR, LACR, and LARS, are introduced to quantify global and component-level abatement costs and rebound effects. Case studies on a coupled IEEE 33-bus power system and 58-node heating network show that the proposed method provides nine feasible strategies in the critical decision region where the weighted-sum method provides only two, with a hypervolume of 0.9412 and a spacing metric of 0.0087. At the selected compromise point, the GACR reaches 0.78 ¥/kg, and under reduced electrical flexibility, the gas-fired boiler’s system-level impact becomes more than 54% higher than that of the combined heat and power unit.
High temperature machines (HTMs) achieve reliable operation in elevated temperatures through the integrated design of materials, insulation, and cooling systems. This capability is crucial for transportation electrification, enabling widespread adoption in cutting-edge applications such as new energy vehicles, electric aviation, and high-speed rail. This paper provides a review of HTMs technology. First, the similarities and differences among various HTMs are outlined. The temperature ranges and technical requirements are defined and categorized in relation to various extreme environments. Then, performance requirements and design challenges across various high temperature application scenarios are compared. Subsequently, a reverse-sequence design process suitable for HTMs is summarized. This process serves as a useful reference for designing HTMs. Finally, the main current technical challenges for HTMs are summarized to advance engineering development for high performance equipment in extreme environments.
A topology-level fault-tolerant (FT) hybrid-type multi-mode isolated resonant DC-DC converter is proposed to realize the high efficiency and enhance the reliability for electric vehicles (EVs). A reconfigurable hybrid multi-mode structure is designed to combine the advantages of the full-bridge converter and the activeneutral-point-clamped three-level converter, and the distributions of voltage and current stresses are optimized and the efficiency is improved. In order to satisfy high-reliability requirements, the operation characteristics of proposed hybrid multi-mode structure are analyzed under short-circuit fault (SCF) and open-circuit faults (OCF) of single power switch. By means of the auxiliary-switches and key power switches reuse strategy, the power stage is reconfigured once the fault is triggered. Consequently, the FT operation is achieved for all single power switch SCFs and OCFs, and the continuous power transfer is maintained, depending on the fault category. Finally, the feasibility and effectiveness of the proposed converter and topology-level fault-tolerant operating mechanism are verified by the detailed experimental results.
In this article, a long horizon temporal information based data-driven predictive current control (LHTI-DDPCC) method is proposed to address the limitations of traditional methods primarily relying on historical input output data for data-driven model updates. To expand the temporal information utilized for updating the data-driven prediction model, the LHTI-DDPCC introduces a generalized disturbance sequence estimator (GDSE) to estimate the lumped disturbance and its high-order derivatives, thereby constructing a disturbance sequence capable of predicting future behaviors of disturbances. Furthermore, a pseudo partial derivative matrix (PPDM) sequence prediction strategy based on an improved auto-regressive with exogenous input (ARX) model is designed. This strategy incorporates the disturbance sequence to capture the dynamic evolution of the parameter matrix, thereby accurately estimating the PPDM sequence under external disturbances. By integrating data-driven control with multi step predictive control, this method utilizes long horizon temporal information to update the data-driven prediction model, thereby achieving receding horizon optimization within the prediction horizon. This significantly enhances prediction accuracy and demonstrates superior control performance. Experimental results validate the effectiveness of the proposed method.
This paper proposes a phase-shifted full-bridge (PSFB) DC-DC converter based on a center-tapped clamp circuit for electric vehicle (EV) on-board charger (OBC) applications. A center-tapped clamp circuit composed of two clamping capacitors and three clamping diodes is proposed, which provides staggered discharge paths for the clamping capacitors. During the charging of the clamping capacitors, the energy stored in the transformer leakage inductance is absorbed, thereby reducing the parasitic oscillation of the rectifier diodes. The staggered discharge mechanism of the two clamping capacitors prolongs the energy transfer time from the clamping capacitors to the load, which improves the voltage gain and enables zero-voltage switching (ZVS) and zero-current switching (ZCS) over a wide loading range. The voltage stress of the rectifier diodes is tightly clamped. The clamping capacitor voltage is also utilized to provide a reverse voltage for rapid primary-side current reset, which reduces the circulating current loss. Based on the detailed analysis of the operating principles, the key parameters of the proposed converter are designed. Finally, a 3.3 kW, 50 kHz experimental prototype was built and tested.
