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The global patents dataset on the vehicle powertrains of ICEV HEV and BEV

机译:ICEVHEV和BEV的车辆动力驱动器上的全球专利数据集

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摘要

Patent bibliometrics data are the most reliable business performance metric for applied research and development activities when investigating the knowledge domains or the technological evolution of vehicle powertrain technologies in the automotive industry. Our paper describes a global patents dataset for the internal combustion engine vehicles (ICEV), hybrid electric vehicles (HEV) and battery electric vehicles (BEV) over 1985–2016. We extracted the patents granted in each powertrain field from Thomson Reuters' Derwent Innovations Index (DII). We applied a combined search strategy of international patent classifications (IPCs) and keywords as well as ‘patent families’ and ‘priority dates’ to construct our global patents dataset. This strategy returned a total of 78,732 patents, within which we identified 49,154 ICEV patents; 10,888 HEV patents; and 18,690 BEV patents. Our database includes numerous descriptive features of each patent such as title, abstract, claim, priority, application and publication dates, IPCs, assignees/applicants, inventors, and cited references. These data are associated with the research article ‘The evolution of dynamic interactions between the knowledge development of powertrain systems’ . The full dataset, which is attached to this article, may be useful to both researchers and practitioners interested in investigating, modelling or forecasting the complexity and evolution of the technical and knowledge domains of the vehicle powertrains, across a variety of instruments such as social network analysis and regression models.
机译:专利性书学资料数据是在研究汽车行业中的知识领域或车辆动力总成技术的技术演变时是最可靠的应用研究和开发活动的商业绩效指标。我们的论文介绍了1985 - 2016年内燃机车辆(ICEV),混合动力电动车辆(HEV)和电池电动车(BEV)的全球专利数据集。我们从汤森路透社德文创新指数(DII)中提取了每个动力总成场授予的专利。我们应用了国际专利分类(IPCS)和关键字以及“专利家庭”和“优先日期”的组合搜索策略来构建我们的全球专利数据集。该策略总共返回了78,732项专利,在其中我们确定了49,154 ICEV专利; 10,888 HEV专利;和18,690个贝夫专利。我们的数据库包括每个专利的许多描述性功能,例如标题,摘要,索赔,优先级,应用程序和出版日期,IPC,受让人/申请人,发明人和引用的参考。这些数据与研究文章“动力系系统知识开发之间的动态相互作用的演变有关。附加到本文的完整数据集可能对有兴趣调查,建模或预测车辆发电机的技术和知识领域的复杂性和演变的研究人员和从业者,跨越社交网络等各种仪器分析和回归模型。

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