1. Introduction
The increase in energy use and related emissions was generated by a higher demand for heat from the residential and commercial sectors and road transport demand; consequently, road transport greenhouse gas (GHG) emissions increased for the second subsequent year, continuing the upward trend in emissions that started in 2014 [
1]. The electrification of mobility is an essential element in a wider strategy for achieving reduced greenhouse gas emissions [
2]. During the previous century, the automotive industry transformed society, bringing new technologies to the market that enhanced their internal combustion engine vehicles, such as global electric vehicles, which are known as one of the most hopeful alternatives for lowering transport-sector carbon dioxide emissions [
3,
4]. An Electric Vehicle (EV) is a road vehicle that includes electric propulsion. With this definition in mind, EVs may include battery electric vehicles (BEV), hybrid electric vehicles (HEV), and fuel-cell electric vehicles (FCEV) [
5]. As sustainable products, FCEVs bring hope for solving several mobility-related problems, as they have no local emissions [
6]. One of the most promising ways to achieve an ideal zero-emissions replacement is to use cleanly produced electricity from non-fossil fuels, such as hydrogen, using fuel-cell technology [
7]. Although FCEVs are promising ways to avoid emissions, both technologies are far from being profitable for car manufacturers [
6]. In addition, the European Commission [
8] set a target for 40% of new cars and vans to be zero- or low-emission vehicles by 2030. Furthermore, in the European Strategic Energy Technology Plan, hydrogen and fuel-cell technologies are identified as the key technologies for achieving GHG reduction targets by 2050 [
9,
10], and in the European Community Research Program, electromobility is a priority. Here, electric vehicle policy focuses mainly on technology optimization and market development, setting future challenges concerning battery and supercapacitor durability, and charging infrastructure, among others [
11]. In turn, the collaborative research and development (R&D) technological projects in Europe are funded by the European Commission [
12], and one of the main priorities for transport research and innovation in Horizon 2020 (H2020) is making transport more sustainable [
13]; for this reason, the European Commission is promoting clean transport, both for electric vehicles by investing in electromobility initiatives and for FCEVs or hydrogen FCEVs, enabling their commercial development by 2020 [
11]. In addition, Edwards et al. [
14] performed a roadmap review of deployment status and targets for fuel-cell applications, such as fuel-cell vehicles, and the annual sales forecast was between 0.4–1.87 million during 2020–2025. In order to help all actors involved in clean transport, it is useful to understand how scientific research is evolving and whether it is having an impact on its technological development.
Bibliometric techniques open the door to a fuller understanding of the scientific research carried out on FCEVs, differing from a conventional literature review: The bibliometric method supplies an innovative, objective perspective through reliable, quantitative processes, and has been broadly used in scientific research as an analytical tool to provide assistance to scholars with a general comprehension of typical research topics [
15]. According to Garousi [
16], bibliometric analysis is a well-established method used to measure publications in a scientific research area, and assessing trends and the value of research is becoming increasingly important [
17,
18]. According to different authors, bibliometrics is approached in various ways and is defined as a research method, or research technique, that allows scientific literature representing extensive global data sets and reliable data to be analyzed and quantitatively measured [
19,
20,
21]. We can take this a little further and introduce the term “scientometrics”, defined by Nowak [
19]: “Scientometrics focuses on the processes occurring in science” [
21]. Scientometrics is the quantitative study of research transfer, and its analysis makes it possible to capture and map scientific knowledge [
21,
22,
23]. Hence, its main objective is to aid the analysis of emerging trends in the knowledge domain [
24]; in addition, knowledge mapping and visualization are meaningful fields of scientometrics [
25].
In the main databases, such as Web of Science and Scopus, there is no bibliometric analysis of the “FCEV” research field. However, other bibliometric analyses have been carried out in research fields related to fuel-cell technology. Kang et al. [
26] define a diffusion model based on bibliometric analysis applied to fuel-cell technologies. Cindrella et al. [
27] present a bibliometric analysis about the field of fuel cells in general from 1992 to 2011, offering a comprehensive overview of trending publications, journals, and countries, and identifying research hotspots through keywords. A similar overview was conducted by Yonoff et al. [
28], identifying research trends in Proton Exchange Membrane Fuel Cells (PEMFCs). Related to energy and fuel research in China, Chen et al. [
29] conducted a bibliometric analysis and, as a result, hydrogen and fuel cells are among the energy research priorities. Bibliometric studies on energy materials related to hydrogen are also relevant, such as the analysis of sodium borohydride (NaBH4) done by Santos and Sequeira [
30], as well as on a lithium mineral developed by Agusdinata et al. [
31]. Considering that FCEVs are a particular field of EV application, Egbue and Long [
32] and Ramirez et al. [
33] carried out bibliometric analyses in order to detect the most relevant research points. In turn, Zhao et al. [
34] depict a bibliometric analysis for EV charging system reliability to analyze the emerging trends of this active research point, such as in EV batteries.
Patents provide an exclusively detailed source of information of inventive activity [
35] and increase the use and commercialization of technologies though market transactions [
36], promoting the diffusion of knowledge and innovation. According to Griliches [
37], patents are one of the most influential proxies for assessing the performance of industry research and development (R&D). For these reasons, the patent is an important tool for investigating a technological development from an economic perspective [
38]. According to Borgstedt et al. [
6], in the automotive industry, patents are the most common way to protect intellectual property, so it can be used as innovative output. Patent data analysis allows us to ascertain the technological state of the studied technology, defining who, when, where, and what is being developed by mining relevant data from patent documents in terms of technology development [
39]. In addition, in this case, in the main databases, such as Web of Science and Scopus, there is no patent data analysis of “FCEV” research articles; nevertheless, other research papers related to patents in fuel-cell technologies have been developed. Related to biohydrogen, Leu et al. [
38] carried out a patent data and citation analysis. With regard to fuel cells, Chang et al. [
40] studied the coactivity between science and technology by analyzing patent–paper pairs. Related to technology forecasting, Chen et al. [
41] defined a model for a patent strategy for fuel-cell technologies using the S-curve method.
In demonstrating the evolution of the scientific–technological research of the FCEV domain, this paper offers a comprehensive assessment of the FCEV research practices which were published in the Scopus database from 1999–2019, in order to identify the key actors in the generation and transmission of knowledge related to FCEVs. As far as technological development is concerned, this paper presents the technological trends of industrial developments, mainly led in this case by the automotive industry, in order to establish, among others, who leads the research and development. All this is done in order to draw the FCEV technology knowledge map, to discuss it with the results of other scientific–technological research studies, and, therefore, to be able to predict the future paths of research trends and foreseeable scenarios.
2. Materials and Methods
The research process, adapted from Bildosola et al. [
42] with some changes, is based on three steps that are intended to define the scientific research activity profile and the technological profile of FCEVs. These steps are developed to answer the questions related to who, where, and what is being or has been researched or developed related to scientific literature and patents.
Figure 1 shows the research approach, revealing that each step has its input and output, creating a flow of information that allows the set objectives to be achieved. In each step, the specific technique used is identified. The stages are developed consecutively, until the scientific and technological profiles are determined. However, the objective of this research process is to be able to cover any type of emerging technology or application.
Profile generation is carried out through the first three steps, and the main tasks to be carried out are explained below.
Step 1. Retrieving data and refining the search. The first assignment is to generate two specific databases concerning scientific publications and patents related to the emerging technology analyzed. Regarding the selection of the scientific database, different studies show that better results are obtained [
43,
44] by using all of the databases (Scopus, Web of Science (WoS), and Google Scholar (GS)). Nevertheless, a very high percentage of WoS and Scopus citations are normally found in GS; those that are not, called unique citations, present a lower scientific impact than WoS and Scopus citations [
45]. Furthermore, the two databases complement each other [
43]; however, in this case, Scopus returns more citations than WoS. For this reason, in the case of FCEVs, the specific databases were generated from Scopus as the scientific database and Lens as the patent database. Scopus is one of largest abstract and citation databases of peer-reviewed literature (75 million documents indexed) [
46], and it was selected to provide scientific publications. Because blooming technologies meet different approaches, the definition of the search query is very important. In bibliometric search strategies, the balance between recall and precision is very important [
47]. However, information scientists usually detect an inverse association between recall and precision [
48]. Therefore, the query was built using “fuel-cell vehicle/car/automobile” as author keywords, obtaining a highly precise query. In addition, to achieve greater recall, the search for the same terms based on the index terms is added to the query (see
Table 1). Index terms are derived from thesauri that Elsevier owns and are added to improve search recall [
46]. The data collection time span was established between 1999 and 2019. In addition, according to Hawkins [
49], gatekeeping publications, such as Abstracting and Indexing (A&I) publications, are the main way to identify advances of a technology. Therefore, the query is in line with the document type: Journal Article and Conference Proceeding. The main query used in the Scopus database retrieved a total of 2514 articles and conference papers for the defined time span. The patent analysis was carried out using Lens, a complete open-source global patent database and research platform containing the world’s most comprehensive full-text patent collection. The search for patents relating to FCEVs was carried out on the basis of Cooperative Patent Classification (CPC), also explained as the patent’s field of technological application. Specifically, the search is directed by the classification Y02T90/34—fuel-cell-powered electric vehicles—which makes the results of localized patents far more accurate. The Y CPC was created in order to give a technological application coverage to new technologies that are not included in the International Patent Classification (IPC); Y: General tagging of new technological developments. Y02T90: Technologies for climate-change mitigation or adaptation related to transportation—enabling technologies with a potential or indirect contribution to GHG emission mitigation. In addition, the search was limited to the patent priority year (or the year in which the patent was invented) in the period from 1999 to 2019.
Step 2: Cleaning up the refined database. This second task includes the use of text mining tools. The scientific database and the patent database were imported into the Vantage Point (VP) software [
50]. VP works with search results from text databases. VP’s capabilities can be broad after importing raw data from scientific databases. VP includes powerful data cleaning tools based on a thesaurus or fuzzy matching techniques. Furthermore, VP integrates powerful techniques for analyzed data, such as natural language programming, a co-occurrence matrix, Principal Component Analysis (PCA), Social Network Analysis (SNA), clusters, and other capabilities for visualizing data. Scientific publications obtained from the previous step were integrated into the database, and in specific fields, such as authors, affiliations, journals, and authors’ keywords, a fuzzy matching was applied in order to group the variations of a word (plurals, acronyms, and similar expressions, among others) that convey the same meaning.
Step 3: Generating the profile. The profile is divided into two parts: The scientific research profile and the technological profile. The scientific profile is based on the literature profile and research community profile, and these describe the research activity in terms of publication trends, academic performance, research topics, and sources of knowledge. The technological profile deals with issues related to patent trends, the main countries of jurisdiction, inventors and applicants, and the main technological fields. To facilitate the analysis of scientific–technological trends, an analysis of networks was carried out, visualizing the collaborative networks between countries, co-authorship networks, keyword co-occurrences, and collaboration networks of applicants and inventors. For this, starting from the matrices of co-occurrences, created both statically and dynamically in the VP software (Search Technololy Inc. Atlanta, USA), the networks were generated and visualized through the Gephi software [
51].