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

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 data...

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Bibliographic Details
Main Authors: Amir Mirzadeh Phirouzabadi, David Savage, Karen Blackmore, James Juniper
Format: Article
Language:English
Published: Elsevier 2020-10-01
Series:Data in Brief
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340920309367
Description
Summary: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’ [1]. 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.
ISSN:2352-3409