2.1 Data and method
The literature data in this paper come from the WoS core collection database, a research information platform with various reliable and high-quality literature sources. In order to obtain more relevant literature, we examined the main forms of V2X in advance. After that, the search prerequisites of V2X publications are set as follows: (TS=(V2X) OR TS=(V2G) OR TS=(V2V) OR TS=(V2P) OR TS=(V2B) OR TS=(V2N) OR TS=(V2I) OR TS=(V2U) OR TS=(V2H)) AND TS=(vehicle), with TS as the topic, the language is English, and the literature type is the article. This study compiles all papers released up to October 13, 2022, without any time restrictions. Based on the above search protocol, 4,097 records were retrieved, and 2,989 records remain after eliminating the literature unrelated to the research subject. The primary information and descriptive statistics are shown in Table 1.
Table 1 Descriptive statistics
Description
|
Result
|
Description
|
Result
|
Timespan
|
2003:2022
|
Keywords
|
7,578
|
Documents
|
2,989
|
Cited references
|
68,010
|
Co-authors per doc
|
3.25
|
Cited authors
|
54,812
|
Co-institutions per doc
|
1.61
|
Cited journals
|
33,968
|
Note: The data are obtained by CiteSpace.
The footprint of scientific development can be extracted from published articles. Therefore, bibliometric analysis is widely accepted and used since it provides a great deal of objective information and general opinion through statistical methods (Chen et al., 2022; Donthu et al., 2021). Based on the research objectives of this paper and the types of data reviewed, we selected the appropriate bibliometric methods and techniques for performance analysis and scientific mapping. The bibliometric visual analysis tools used in this paper are CiteSpace (Chen, 2006), Bibliometrix (Aria & Cuccurullo, 2017), and VOSviewer (Van Eck & Waltman, 2010), which can obtain multiplicity, time-sharing and dynamism mapping knowledge domains by co-occurrence analysis.
2.2 Distribution and highly cited research
Following the data collected on the WoS core collection database, the historical distribution of V2X publications is shown in Fig. 1. In general, the annual number of V2X publications shows an increasing trend year by year between 2003 and 2022, indicating that V2X is gradually gaining attention, and this attention is still ongoing.
Based on the number of annual publications and technology development, the publication evolution can be roughly divided into three sub-periods as follows:
1) Initial period (2003–2010): It is obvious that the annual publications on V2X topics were very sparse from 2003 to 2010, and there were even no publications in 2004 and 2006. During this period, more attention was paid to V2G because of the launch of commercial EVs, while other V2X received less attention (Huang et al., 2017). Highly cited articles are shown in Table 2.
Table 2 Highly cited research for the initial period
Research
|
Marginal contribution
|
Key indicators
|
(Kempton & Tomic, 2005a)
|
Analyzed V2G power capacity, revenue and cost
|
Engineering rationale and economic motivation for V2G power
|
(Kempton & Tomic, 2005b)
|
Described the management strategies, business models and three steps for a transition of V2G
|
The amount of V2G to stabilize peak and baseload power.
|
(Lund & Kempton, 2008)
|
Studied V2V communication channels in the 5-GHz band
|
The complexity and accuracy of the channel model
|
(Saber & Venayagamoorthy, 2010)
|
Particle swarm optimization was proposed to balance the cost and emission reductions for V2G.
|
Operation cost and emission
|
(Peterson et al., 2010)
|
Investigated the lifetime performance of relevant commercial Li-ion cells in V2G scenario
|
Battery capacity loss
|
2) Ascent period (2010–2016): During this period, V2X publications increased at a steady rate year by year. At this stage, the appearances of connected cars, 4G LTE technology and the EV market have raised expectations for smart cities. As a result, the interactive and predictive capabilities of V2X have gained a great deal of attention, but are mainly focused on V2V and V2G (Debnath et al., 2014). Table 3 shows representative literature from this period.
Table 3 Highly cited research for the ascent period
Research
|
Marginal contribution
|
Key indicators
|
(Sortomme & El-Sharkawi, 2011)
|
An V2G algorithm for unidirectional regulation was developed for use by an aggregator
|
Aggregator profit, average level of power draw for EV charging
|
(Madawala & Thrimawithana, 2011)
|
Proposed a novel current-sourced bidirectional inductive power transfer power interface for V2G
|
Direction and amount of power flow, the magnitude and relative phase of voltages
|
(Sortomme & El-Sharkawi, 2012)
|
Developed an optimal scheduling algorithm for V2G energy and ancillary services
|
Aggregator profit, system flexibility and peak load shaving, customer cost
|
(Wang et al., 2012)
|
Introduced the information spread problem in a joint V2I and V2V communication system
|
System throughput, packet error ratio
|
(Yamazato et al., 2014)
|
Investigated an image sensor based visible light communication system for application in V2I and V2V
|
Integrating safety application
|
(Hu et al., 2015)
|
Researched an EV switched-reluctance motor drive powered by a battery/supercapacitor with V2G capabilities
|
The characteristic of acceleration, deceleration, reversible driving and braking
|
(Erdinc et al., 2015)
|
Presented a mixed-integer linear programming framework-based modeling for V2G and V2H energy trading
|
Power cost reduction
|
(Zheng et al., 2016)
|
Directed graphs were adopted to describe radar-based sensors V2V communications
|
Performance of unstable
|
(Liu et al., 2016)
|
Provided a data dissemination system via the hybrid of I2V and V2V communications
|
Broadcast productivity
|
3) Prosperity period (2016–now): After 2016, the annual article volume of V2X has been high and growing faster. During this period, the rapid development of technologies such as artificial intelligence, 5G and blockchain, along with decentralized processing of V2X data, have reduced computational complexity and enabled real-time computing and intelligent decision-making in cities to become a reality gradually (Yaqoob et al., 2020). The highly cited literature at this stage is shown in the Table 4.
Table 4 Highly cited research for the prosperity period
Research
|
Marginal contribution
|
Key indicators
|
(Ye et al., 2019)
|
Investigated a decentralized resource allocation mechanism for V2V and V2I communications based on deep reinforcement learning
|
V2I capacity, V2V latency
|
(Liang et al., 2017)
|
Developed the spectrum sharing and power allocation design for V2V and V2I communications
|
The reliability of V2V links, the capacity of V2I links
|
(Thomas et al., 2018)
|
Introduced a two-way communication V2B and V2G energy management framework for a smart building
|
The total cost of the system
|
(Liang et al., 2019)
|
Proposed a distributed resource sharing scheme based on multi-agent reinforcement learning for V2V and V2I links.
|
Sum capacity of V2I links, payload delivery rate of V2V links.
|
(Gonzalez-Martín et al., 2019)
|
Presented the first analytical models of the communication performance of C-V2X or LTE-V Mode 4.
|
Transmission power, packet transmission frequency, modulation and coding schemes, traffic densities
|
(G. Xu et al., 2019)
|
Developed an V2E computation offloading method with privacy preservation for V2I and V2V
|
Execution time, energy consumption
|
(Zhou et al., 2020)
|
Introduced a secure and efficient V2G energy trading framework combining blockchain, edge computing and contract theory
|
Contract feasibility, social welfare, efficiency of task offloading, successful probability
|
(Moubayed et al., 2021)
|
Formulated the problem of optimal V2X service placement in a hybrid core/edge environment as a binary integer linear programming problem
|
Average delay/latency, average computing resource utilization
|
(Zhao et al., 2022)
|
Proposed a collaborative vehicle data correction method based on rationality and Q-learning techniques for V2X
|
Recall rate, accuracy rate, average error/ correction distance
|
In the future, with the gradual practice of smart cities, V2X documents may reach a climax period. In addition, it is worth mentioning that the emergence of new sub-periods in V2X publications always lags 1-2 years behind the time when new technologies or concepts are proposed, which is a phenomenon in line with common sense.
2.3 Regional collaboration and prolific area
From a macro perspective, we study the impact of each country/region on V2X research and the scientific collaboration network. As shown in Fig. 2, China contributes the highest output of V2X publications, followed by the USA and the UK. The USA has the highest impact on V2X publications, followed by China and the UK. The research in these prolific regions often plays a leading role in other countries. Not surprisingly, there is a solid cooperative spirit in all three of these highly cited countries. It is worth noting that geographic location affects the research interests in different countries/regions to some extent (Qin et al., 2022).
From a meso perspective, we count the research areas covered by V2X publications. As shown in Table 5, the published articles mainly focus on Engineering Electrical Electronic, Telecommunications and Computer Science Information Systems areas. It indicates that many scholars are keen on studying information technology issues related to V2X, such as V2X sensing technology, communication protocols, and how 5G, Internet of Things (IoT), blockchain and other high technologies apply to V2X.
Table 5 The top 10 research areas concerning V2X
Rank
|
Publication
|
Research area
|
Start year
|
%
|
1
|
1,742
|
Engineering Electrical Electronic
|
2003
|
58%
|
2
|
1,199
|
Telecommunications
|
2003
|
40%
|
3
|
733
|
Computer Science Information Systems
|
2005
|
25%
|
4
|
727
|
Transportation Science Technology
|
2005
|
24%
|
5
|
354
|
Energy Fuels
|
2005
|
12%
|
6
|
265
|
Engineering Civil
|
2005
|
9%
|
7
|
149
|
Instruments Instrumentation
|
2010
|
5%
|
8
|
111
|
Computer Science Hardware Architecture
|
2009
|
4%
|
9
|
107
|
Chemistry Analytical
|
2011
|
4%
|
10
|
95
|
Physics Applied
|
2010
|
3%
|
Note: Some interdisciplinary research may involve multiple research areas.
2.4 Theme evolution of V2X research
In order to clarify the evolution of V2X research topics, we analyzed the author keywords and keywords plus of the collected papers, as shown in Fig. 3.
In general, “V2G” and “V2V” have attracted much attention in all sub-periods. During the initial period, the research themes were sparsity mainly involving the initial exploration of “V2G” and “V2V”. There were more high-frequency research themes during the ascent period than in the initial period, and the research themes showed a diversity characteristic. Some new problems appeared in this stage, such as “V2I communication”, “resource allocation” and “traffic management”, which were analyzed by methods such as “model predictive control”, “performance analysis” and “optimization algorithm”. After that, hot research themes of V2X have been less and centralized, mainly manifest as the optimization and control of V2X, especially V2G and V2V. To summarize, the theme evolution of V2X research shows the process of “sparsity—diversity—concentration”.
2.5 Term cluster of V2X research
To better understand the research hotspots in V2X publications at different periods, we extracted terms from the title, abstract, author keywords and keywords plus and performed cluster analysis based on CiteSpace. Fig. 4 shows a timeline diagram of terms with the top six clusters: #0 electric vehicle, #1 vehicular network, #2 resource allocation, #3 automated vehicle, #4 to-vehicle channel model and #5 intelligent transport system.
In clusters, Cluster #0 electric vehicle is the largest and most active cluster. Cluster #4 to-vehicle channel model and Cluster #5 intelligent transport system are no longer active in recent years, while the others are still hot. Regarding the periods, all clusters started before 2010 but were not very active then. The earliest research was on “intelligent transport system”, with research hotspots such as “vehicular network” and “intelligent transportation”. During 2010–2016, terms with the most prominent occurrences included “electric vehicle”, “power system”, “vehicular ad hoc network”, etc. These research hotspots have been continued until now and have generated a lot of connections with subsequent research. After 2016, the research terms have become more refined, and their frequency has been more balanced.
According to the frequency of the terms, “electric vehicle” and “vehicular network” are the two most active clusters and hottest terms. Among them, “electric vehicle” is a symbol of the electrification of transportation and a key player in the smart grid, and it will contribute to the evolution of V2X towards EV2X (electric vehicle-to-everything) (T. D. Mo et al., 2022). The “vehicular network” can facilitate a faster interchange of V2X information, which is the inevitable requirement to realize the safety and efficiency of smart transportation (Shah et al., 2022).
Depending on the keywords of the clusters, we categorize V2X applications into three scenarios. Cluster #1 vehicular network, Cluster #2 resource allocation and Cluster #4 to-vehicle channel model are categorized as communication scenario. Cluster #3 automated vehicle and Cluster #5 intelligent transport system refer to the computation scenario, and Cluster #0 electric vehicle correspond to the service scenario. The forms, supporting technologies, and challenges of V2X in these three scenarios are described in detail later.