Publications by authors named "Xinghu Li"

With the ever-growing digitalization and mobility of electric transportation, lithium-ion batteries are facing performance and safety issues with the appearance of new materials and the advance of manufacturing techniques. This paper presents a systematic review of burgeoning multi-scale modelling and design for battery efficiency and safety management. The rise of cloud computing provides a tactical solution on how to efficiently achieve the interactional management and control of power batteries based on the battery system and traffic big data.

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This paper focuses on oxidation reactivity and nanostructural characteristics of particulate matter (PM) emitted from diesel engine fuelled with different volume proportions of diesel/polyoxymethylene dimethyl ethers (PODE) blends (P0, P10 and P20). PM was collected using a metal filter from the exhaust manifold. The collected PM samples were characterized using thermogravimetric analysis (TGA), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Raman spectroscopy.

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In this study, the analgesic and sedative efficacy of Chuanxiong essential oil after nasal administration was compared with that of the commonly used oral administration route. The essential oil significantly reduced nociception only 5 min after nasal administration but not by i.g.

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Synopsis of recent research by authors named "Xinghu Li"

  • - Xinghu Li's recent research primarily focuses on the interactional management and safety of power batteries in electric vehicles, emphasizing the impact of digitalization and advanced materials on battery performance and safety.
  • - A systematic review presented in his 2023 paper highlights how multi-scale modeling, coupled with traffic big data and cloud computing, can offer solutions for efficient battery management in electric transportation.
  • - In earlier work, Li investigated the oxidation reactivity and nanostructural characteristics of particulate matter emitted from diesel engines using fuel blends, applying advanced characterization techniques such as TGA, SEM, and TEM to advance understanding of emissions from alternative fuels.