Dataset on the global patent networks within and between vehicle powertrain technologies — Cases of ICEV, HEV, and BEV

The emergence of networks is a crucial channel for automotive organisations to build and diffuse the required environmental innovations in the transportation sector and accelerate the transition to the green mobility economy. This article contains the dataset regarding the global patents networks shaped both within and between the three vehicle powertrains of internal combustion engine vehicle (ICEV), hybrid electric vehicle (HEV) and battery electric vehicle (BEV) for the period of 1985–2016. The data was acquired from Thomson Reuters' Derwent Innovations Index (DII) platform using the elements of ‘patent families’ and ‘priority dates’. We describe the dataset for the three major automotive periods of ‘towards sustainable mobility’ (1985–1996), ‘towards hybridisation’ (1997–2007), and ‘towards mass commercialisation’ (2008–2016). The dataset bears on two levels, individual and mutual, and we used a separate combined search strategy of keywords and IPCs codes (international patent classification) for each level. At individual level, we explored the internal network features of each powertrain individually (i.e. ICEV, HEV, and BEV). Monitoring a total of 78,732 patents in the three individual powertrain networks, we discovered a total of 1856 unique parent organisations connecting vis-à-vis 5849 bilateral relationships and operating around 4450 joint patents. At mutual level, we explored the mutually common network features of the powertrains (i.e. ICEV-HEV, HEV-BEV, and BEV-ICEV). Monitoring a total of 4702 patents in the three mutual powertrain networks, we discovered a total of 102 unique parent organisations connecting vis-à-vis 384 bilateral relationships and operating around 303 joint patents. These organisations were found specialised around 435 unique subgroup-level IPC codes, of which 134 codes were related to environmentally friendly innovations. The dataset presented in this article is used in [1] and allows researchers not only to map and model the network dynamics and structures within and between the powertrains at global level, but also to analyse and forecast their knowledge flows, technical domains and environmental innovations aspect, using a wide range of models such as social network analysis or regression.