Tao Chen

1 month ago | nature.com | Yuanshi Zhang |Tongxin Xu |Tao Chen |Qinran Hu |Hongrui Chen |Xukun Hu | +1 more

The charging transaction data of electric vehicle (EV) users is crucial for studying charging market dynamics and formulating effective policies. However, due to factors such as the privacy of EV users and the complex coupling relationships between charging dealers, existing EV charging transaction datasets are plagued by issues such as incompleteness, significant bias, and a lack of real-time information. To address these issues, a real-time charging transaction dataset has been created, comprising 441,077 charging transactions collected from 13 charging stations in China over a 2-year period. The dataset includes detailed data of EV user charging transaction time, price and charging status, as well as the charging termination reasons and weather data for each charging session. This dataset offers references for identifying EV user behaviors and extracting charging fault factors from multiple aspects, supporting research applications in EV charging facility planning, EV charging and discharging management, and charging economic evaluation.

2 months ago | mdpi.com | Tao Chen |Liyuan Zhao |Zhou Ya |Zihao Yu

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.

Feb 5, 2025 | dx.doi.org | Yawen Chen |Yan Qiu |Tao Chen |Hong Wang

Jan 24, 2025 | onlinelibrary.wiley.com | Tao Chen |Shujing Li |Ziyuan Wang |Engineering Kunming

Conflict of Interest The authors declare no conflict of interest. Supporting Information Filename Description smll202406695-sup-0001-SuppMat.docx4.1 MB Supporting Information References 1, Science 2008, 321, 1457. 2, , Science 2020, 367, 1196. 3, Science 1999, 285, 703. 4, , , , Adv. Energy Mater. 2017, 8, 1701797. 5, , , , , , Nature 2011, 473, 66. 6, , , , , , , , , , , , Science 2021, 373, 556. 7, , , , , , , , , Adv. Funct. Mater. 2023, 33, 2302770.

Jan 14, 2025 | devblogs.microsoft.com | Tao Chen

AI Connectors in Semantic Kernel are components that facilitate communication between the Kernel’s core functionalities and various AI services. They abstract the intricate details of service-specific protocols, allowing developers to seamlessly interact with AI services for tasks like text generation, chat interactions, and more. Developers utilize AI connectors to connect their applications to different AI services efficiently.