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A novel asymmetric spoke-type interior permanent magnet (AS-IPM) machine is proposed in this paper. It utilizes the magnetic-field-shifting (MFS) effect to improve the torque performance, which achieves a high utilization ratio of both permanent magnet (PM) torque and reluctance torque. In addition, a general pattern of rotor topologies is proposed to represent all possible machine structures. Various rotor structures can be obtained by changing the design parameters of the general pattern. A non-dominated sorting genetic algorithm II (NSGA-II) is adopted to automatically search for optimal rotor configurations. With the aid of the optimization program, an asymmetric spoke-type rotor structure with improved performance is obtained. To showcase the advantages of the proposed machine, the electromagnetic performance is compared between a conventional spoke-type interior permanent magnet (S-IPM) machine and a proposed AS-IPM machine. The finite-element simulation results show that the optimal design of the AS-IPM performs a 7.7% higher output torque ripple due to the MFS effect while the total PM volume remains the same. Meanwhile, the torque ripple of the proposed structure is significantly reduced by 82.1%.

The ice-templated method (ITM) has drawn significant attention to the improvement of the electrochemical properties of various materials. The ITM approach is relatively straightforward and can produce hierarchically porous structures that exhibit superior performance in mass transfer, and the unique morphology has been shown to significantly enhance electrochemical performance, making it a promising method for energy storage and conversion applications. In this review, we aim to present an overview of the ITM and its applications in the electrochemical energy storage and conversion field. The fundamental principles underlying the ITM will be discussed, as well as the factors that influence the morphology and properties of the resulting structures. We will then proceed to comprehensively explore the applications of ITM in the fabrication of high-performance electrodes for supercapacitors, batteries, and fuel cells. We intend to find the key advances in the use of ITM and evaluate its potential to overcome the existing challenges in the development of efficient energy storage and conversion systems.

The performance of a polymer electrolyte membrane fuel cell (PEMFC) closely depends on internal reactant diffusion and liquid water removal. As one of the key components of PEMFCs, bipolar plates (BPs) provide paths for reactant diffusion and product transport. Therefore, to achieve high fuel cell performance, one key issue is designing BPs with a reasonable flow field. This paper provides a comprehensive review of various modifications of the conventional parallel flow field, interdigitated flow field, and serpentine flow field to improve fuel cells’ overall performance. The main focuses for modifications of conventional flow fields are flow field shape, length, aspect ratio, baffle, trap, auxiliary inlet, and channels, as well as channel numbers. These modifications can partly enhance reactant diffusion and product transport while maintaining an acceptable flow pressure drop. This review also covers the detailed structural description of the newly developed flow fields, including the 3D flow field, metal flow field, and bionic flow field. Moreover, the effects of these flow field designs on the internal physical quantity transport and distribution, as well as the fuel cells’ overall performance, are investigated. This review describes state-of-the-art flow field design, identifies the key research gaps, and provides references and guidance for the design of high-performance flow fields for PEMFCs in the future.

Latent heat thermal energy storage (TES) plays an important role in the advocation of TES in contrast to sensible energy storage because of the large storage energy densities per unit mass/volume possible at a nearly constant thermal energy. In the current study, a heat exchange device with a zigzag configuration containing multiple phase-change-materials (m-PCMs) was considered, and an experimental system was built to validate the model for a single PCM. A two-dimensional numerical model was developed using the ANSYS Fluent 14.0 software program. The energy fractions method was put forward to calculate the average Ste number and the influence of Re and Ste numbers on the discharge process were studied. The influence of phase change temperature among m-PCMs on the solidification process has also been studied. A new boundary condition was defined to determine the combined effect of the Re and Ste numbers on the discharging process. The modelling results show that for a given input power, the Ste (or Re) number has a significant impact on the discharging process; however, the period value of inlet velocity has almost no impact on it. Besides, the zigzag plate with m-PCMs has a good impact on the temperature shock as “filter action” in the discharging process.

Salt cavern flow batteries (SCFBs) are an energy storage technology that utilize salt caverns to store electrolytes of flow batteries with a saturated NaCl solution as the supporting electrolyte. However, the geological characteristics of salt caverns differ significantly from above-ground storage tanks, leading to complex issues in storing electrolytes within salt caverns. Therefore, investigating and summarizing these issues is crucial for the advancement of SCFB technology. This paper’s innovation lies in its comprehensive review of the current state and development trends in SCFBs both domestically and internationally. First, the current development status of SCFB energy storage technology both domestically and internationally is summarized. Then, eight main issues are proposed from the perspectives of salt cavern geological characteristics (tightness, conductivity, ions, and temperature) and electrolyte properties (selection, permeability, corrosion, and concentration). Finally, a novel SCFB system is proposed to address the most critical issue, which is the low concentration and uneven distribution of active materials in the current SCFB system. The review in this paper not only comprehensively summarizes the development status of SCFBs both domestically and internationally, but also points out the direction for the future research focussing on SCFBs.

China faces a difficult choice of maintaining socioeconomic development and carbon emissions mitigation. Analyzing the decoupling relationship between economic development and carbon emissions and its driving factors from a regional perspective is the key for the Chinese government to achieve the 2030 emission reduction target. This study adopted the logarithmic mean Divisia index (LMDI) method and Tapio index, decomposed the driving forces of the decoupling, and measured the sector’s decoupling states from carbon emissions in Xinjiang province, China. The results found that: (1) Xinjiang’s carbon emissions increased from 93.34 Mt in 2000 to 468.12 Mt in 2017. Energy-intensive industries were the key body of carbon emissions in Xinjiang. (2) The economic activity effect played the decisive factor to carbon emissions increase, which account for 93.58%, 81.51%, and 58.62% in Xinjiang during 2000–2005, 2005–2010, and 2010–2017, respectively. The energy intensity effect proved the dominant influence for carbon emissions mitigation, which accounted for −22.39% of carbon emissions increase during 2000–2010. (3) Weak decoupling (WD), expansive coupling (EC), expansive negative decoupling (END) and strong negative decoupling (SND) were identified in Xinjiang during 2001 to 2017. Gross domestic product (GDP) per capita elasticity has a major inhibitory effect on the carbon emissions decoupling. Energy intensity elasticity played a major driver to the decoupling in Xinjiang. Most industries have not reached the decoupling state in Xinjiang. Fuel processing, power generation, chemicals, non-ferrous, iron and steel industries mainly shown states of END and EC. On this basis, it is suggested that local governments should adjust the industrial structure, optimize energy consumption structure, and promote energy conservation and emission reduction to tap the potential of carbon emissions mitigation in key sectors.

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.