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Review

Electromobility: Logistics and Business Ecosystem Perspectives Review

by
Sebastian Szymon Grzesiak
1 and
Adam Sulich
2,*
1
Faculty of Management, Wroclaw University of Economics and Business, Komandorska Str. 118/120, 53-345 Wroclaw, Poland
2
Department of Advanced Research in Management, Faculty of Management, Wroclaw University of Economics and Business, Komandorska Str. 118/120, 53-345 Wroclaw, Poland
*
Author to whom correspondence should be addressed.
Energies 2023, 16(21), 7249; https://doi.org/10.3390/en16217249
Submission received: 3 September 2023 / Revised: 15 October 2023 / Accepted: 23 October 2023 / Published: 25 October 2023
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Abstract

:
In the evolving landscape of electromobility, the logistics domain is undergoing significant transformations, reflecting broader changes in both the transport and energy sectors. This study aims to present an exploration of the scientific literature indexed in Scopus dedicated to electromobility logistics and the business ecosystem. The methods used in this article include a classical literature review and a systematic literature review, combined with bibliometric analysis in VOSviewer software (version 1.6.19). These methods allowed for the analysis of keywords and research motifs related directly to the development of electromobility from a business ecosystem perspective. Results of this study indicate that while technological innovations play a significant role, the success of electromobility is also highly dependent on its efficient and collaborative business ecosystem of entities involved in transportation and energy sectors. This ecosystem, defined by mutual value creation and strategic collaboration along with infrastructure and logistics, has the potential to drive economic growth and create new green jobs in the energy and transport sectors. In conclusion, the study underscores the importance of a sustainable and balanced approach, emphasizing both technological advancements and the significance of a robust business ecosystem for the future of the electromobility business ecosystem.

1. Introduction

With the unprecedented rise of electromobility, the logistics landscape is undergoing transformative changes [1,2], reflecting the shifting paradigms in both the transport [3] and energy sectors [4,5]. While much discourse on electromobility emphasizes environmental issues, labor market dynamics [6], and social concerns, economic implications often receive less attention [3,7]. This article endeavors to fill that gap by focusing on the logistics and economic intricacies embedded within the concept of the ‘business ecosystem’ [8]. This ecosystem, defined by mutual value creation and strategic collaboration along with infrastructure and logistics, has the potential to drive economic growth and create new green jobs in the energy [9] and transport sectors [10].
As the integration of electric vehicles and related infrastructure [11,12] becomes increasingly widespread [13,14], understanding the evolution of logistics within this context becomes paramount [15,16]. The energy and transport sectors are essential elements of every country’s economy [15,17]. Therefore, the focus on the logistics of electromobility represents broader systemic shifts [18,19] in how businesses operate [20,21], interact [17,22,23], and innovate within these sectors [20,24,25]. This article also aims to guide technological development and improvements for the broader economy.
Electromobility poses new challenges for the energy sector [26,27], especially as the sector grapples with the intricacies of implementing automation [13,28] through the Internet of Things (IoT), minimizing operational costs [29,30], and maximizing the utilization of electric vehicles [31,32]. On the other hand, electromobility also introduces new challenges to the power grid charging power demand changes [33,34], which vary during the day and vehicle types energy consumption [35,36]. An in-depth look at the European Union context uncovers complexities such as varied power standards [35,37,38], location-specific emissions [39,40], and the meticulous task of route planning based on available infrastructure and workforce [10,41]. Additionally, with the emergence of technologies like Artificial Intelligence (AI), there is a notable emphasis on process automation, including the vital aspects of planning [11,42], strategy, and decision-making [34,43,44]. This evolving landscape provides businesses with the opportunity to focus on the systematic interconnections between individual components, fostering a more unified and integrated approach [45,46,47]. It can also point the way toward technology development and improvement for the whole economy [14,47,48]. In this complex landscape of electromobility, two perspectives are gaining popularity in both business practice and theory [4,12]: logistics and the business ecosystem [6,28]. Those two views help to understand the connections between the transportation and energy sectors [49,50].
Currently, researchers predominantly focus on the technical, computational, and monothematic aspects of specific challenges within electromobility. However, there is a notable absence of exploration into non-technical, cross-disciplinary, and business aspects. While the primary aim of many organizations is profit generation, a holistic approach considering ecosystems and business objectives is vital. To address this research gap, this article is presented. It offers a multi-faceted analysis of electromobility, encompassing related ecosystems such as energy sectors, logistics, and emerging trends.
Moreover, beyond mere operational logistics lies the complex business ecosystem of electromobility [51,52]. There has been a growing emphasis on this aspect in recent years, suggesting that the success of electromobility is closely linked with the vibrancy of its business ecosystem [53,54]. However, like any significant transition, this evolution comes with its challenges [55,56]. Companies are compelled to adapt to innovative solutions [57,58], influenced not only by technological advances but also by social, legal, and environmental factors [59,60]. This increased focus on the electromobility ecosystem underscores that its success is not solely attributed to technological development [2,14], both in the transport and energy sectors [61,62], but is also deeply anchored in the cooperative and innovative spirit that permeates its business ecosystem [63,64]. The development perspective of this ecosystem highlights not just growth [42,65], but a forward-thinking pattern of cooperation, collaboration, and co-innovation [66,67]. Recognizing the business ecosystem from this development viewpoint means acknowledging synergistic relationships, mutual dependencies [68,69], and the creation of shared value among the diverse stakeholders in the e-mobility sector [70,71]. Therefore, there is a need to explore electromobility logistics and business ecosystem perspectives, indicate possible research gaps existing in this field, present and organize knowledge in this area, and propose new research avenues.
This paper aims to provide a comprehensive exploration of the scientific literature indexed in Scopus dedicated to logistics and business ecosystem aspects of electromobility as a combination of the transport and energy sectors. This study offers insights into the cooperation frameworks, challenges, and opportunities that are shaping this burgeoning economic sector. The goal of this paper has been achieved with two complementary methods: classical literature review and systematic literature review [72,73]. A unique feature of this study is the comparative analysis of the findings from these two methods, presented towards the article’s conclusion. The primary source for the bibliometric research comprised publications indexed in the Scopus scientific database. We used VOSviewer software (version 1.6.19; Centre for Science and Technology Studies, Leiden University: Leiden, The Netherlands) in the form of bibliometric maps [74,75] and detailed tables that outline perspectives on e-mobility logistics and its business ecosystem.
The structure of this paper follows a classical format, encompassing the Introduction, Literature Review, Materials and Methods, Results, Discussion, and Conclusions. Consequently, the primary content is organized into six sections. Section 1 serves as the introduction, highlighting the aim and motivation behind the study. Section 2 delves into a classic literature review, spotlighting selected determinants of electromobility development. Key focal points in this section include accessibility, the trajectory of electromobility development, and the infrastructure for electric cars, which are seen as convergence points between the transportation and energy sectors. Section 3 reveals the research method employed: the systematic literature review. Within this section, a figure illustrating dependent sectors is incorporated, and a table juxtaposing the classic literature review with the bibliometric approach is also provided. This is followed by an analysis and presentation of the results drawn from the exploration of the Scopus database (Section 4). A comprehensive discussion of the findings ensues, during which the limitations of the methods used are underscored (Section 5). The paper concludes by addressing implications for both theory and practice and then proposing avenues for future research (Section 6).

2. Literature Review

Significant and dominant trends can be observed in the global economy [76], influencing not only the present but also the future of the transport and energy sectors [41,77]. Changes in the energy sector drive development [28,78] and shifts in other parts of the economy [79,80], as there is a business ecosystem operating across various economic sectors [70,81]. With increasing globalization, these trends are becoming similar and even unified across the developed world [82,83]. Development trends observed on a global scale are called megatrends [84,85]. Highly developed countries are at the forefront of the introduction of modern solutions, while developing countries tend to rely on their implementation [86,87]. An example of this phenomenon can be seen in the charging station networks in the European Union, and their development is at the core of the electromobility trend highlighted and the trends accompanying logistics [78,88]. Notable is the logistics trend radar proposed by the company DHL [84]. The trend radar (Figure 1) is a graphical analysis of various trends that illustrates the links between logistics processes and social aspects.
The megatrends in logistics are illustrated in Figure 1, focusing on the future of logistics. While the radar was developed based on current events and business environment observations, Figure 1 offers two trend perspectives. These are categorized within the radar into two distinct areas: the near future, up to 5 years (represented by the internal part of the circle in light yellow), and the further future, between 5 and 10 years (depicted in dark yellow). Additionally, the overview is bifurcated into two primary directions: the first, highlighted in green, denotes social and business trends, while the second, highlighted in blue, underscores technology trends [27,89]. Figure 1 also shows the hierarchy of trends based on their impact. At the bottom of Figure 1, low-impact trends are indicated, and with their higher position on the radar, they are more relevant, and at the top, they have a high impact on shaping the reality of the logistics sector [90]. While the radar indicates logistics trends, areas such as alternative energy solutions, big data analytics [91,92], decarbonization, and interactive AI have connections with the energy and transportation sectors [93]. Together, they form a business ecosystem perspective [94,95].
There are a few interesting implications for Figure 1. The analysis of the importance of the presented key points of logistics development leads to the identification of the following trends influencing electromobility:
  • Decarbonization collectively describes measures to reduce the amount of carbon dioxide (CO2) generated in logistics operations and shift towards climate neutrality [96,97]. This trend has a high impact because the problem of high emissions is particularly relevant for logistics [44], which in supply chains accounts for 60% of global emissions [98]. Surprisingly, this trend has been placed in the social and business areas of Figure 1, whereas actions including aiming for lower energy consumption during transport and the introduction of low-carbon power sources are technological [99].
  • Sharing economy refers to the rental or lending of movable and immovable objects based on a relatively short period of use by a single entity, with a focus on the rotation of users [100]. In the sharing economy, the owners become organizations rather than individuals [101]. On the other hand, clients are users and do not have to at least service the rented objects, but they do not have any rights to them beyond the period of the loan [102].
  • Smartification brings digital solutions wherever technology allows [103,104]. It enables devices to be networked and communicate with each other [105]. One example is the car key, which can be replaced by a mobile application. A characteristic feature is wireless connections via networks, such as WiFi, GSM, and Bluetooth [106]. Despite its characteristics, smartification has been noted among social and business trends in Figure 1.
  • Alternative energy solutions, or renewable energy sources (RES), are technological trends influencing the logistics of electromobility [107,108]. The development of electromobility is related to investments in new technologies for solar, wind, or hydropower [109], expanded to include aspects of energy efficiency, energy storage, and current closed-loop energy use [110]. Alternative energy solutions influence both the logistics and energy sectors [82,111].
  • Physical internet is a completely new and comprehensive view of logistics models operating worldwide that involves connecting processes physically, digitally, and operationally [106,112]. In practice, it means relying on full communication and streamlining all logistics processes in real time [113]; however, this trend is based on the technology development [114]. It affects the social and business spheres from a longer perspective (Figure 1).
  • Quick commerce involves the digitalization of shopping combined with fast local deliveries, even within an hour [115,116]. This, for example, can be combined with fast multimodal delivery at the next level, ensuring delivery of all sorts of products [117,118].
  • Cybersecurity 2.0 signifies the next level of network security, incorporating AI activities to better automate the detection of potential threats [119,120].
Listed megatrends in logistics also influence the functioning of the whole business ecosystem and electromobility infrastructure development accordingly [121,122]. The importance of electromobility logistics can be analyzed in terms of the other key trends presented in Figure 1. This is because the development of charging stations and electric cars is the result of a combination of different logistics trends and is based on their interaction [123]. This mutual synergy has been observed in the logistics trends radar proposed by DHL.
Decarbonization is a general trend leading the way in the use of alternative fuels in motor vehicles [124,125]. The lack of direct CO2 emissions or their significant reductions in transport is recognized as an advantage over combustion cars [122,126]. This is an asset in city centers, which are characterized by high air pollution [122,127,128]. Electric vehicles, also known as zero-emission vehicles [63,129], are a response to the growing importance of the decarbonization trend, as presented in Figure 1.
The sharing economy is emerging progressively in the Eastern European markets [130,131]. While Western Europe is experiencing rapid growth [59,101], the eastern part is still primarily based on the ownership of things [127,132]. New innovations can be seen in the larger cities, where shared mobility is growing [133]. Carpooling, city bicycles, electric scooters, and even taxis rented via applications are gradually becoming part of everyday economic life across Europe [39,134]. What is more, the idea of the so-called Smart City, which is gaining popularity, also involves shared goods [135,136]. It would not be so easy if it were not for progressive digitization and smartification, which are also mentioned in Figure 1. In line with these, basic manual activities are being replaced by automated solutions that can be accessed from a smartphone [137]. The move away from cash in logistics processes is also intensifying, and it is also becoming more common to move away from physical payment cards and replace them with digital versions on phones, watches, or wristbands. This removes the operational time of the activities undertaken and introduces the possibility of collecting more data [138]. What is more, individual devices can communicate and transmit data in real time. From a logistics point of view, they are an asset because of their ability to accurately locate errors and make improvements [19]. Such high-tech solutions are often accompanied by alternative energy solutions that go beyond the basic definition of RES and turn the focus towards ensuring electricity supply and energy stability [127]. With the increasing electrification of vehicles, the demand for non-standard (increased) connection capacities is growing [139,140]. This involves not only the expansion of the energy infrastructure but also the provision of adequate power. Unfortunately, RES are characterized by instability of energy production and varying efficiency [141], e.g., due to geographical location [142]. Therefore, protection against the loss of so-called blackouts from RES is being sought through energy storage and the creation of closed energy circuits. Blackouts are temporal disruptions to electricity supply [143].
A closed energy cycle assumes that the electricity produced by RES is used on an ongoing basis, e.g., to charge an electric vehicle connected to a station equipped with, for example, photovoltaic panels [37,112]. Thanks to the mutual exchange of information between the vehicle, charging station, and energy infrastructure (IoV), it is possible to adjust the individual power output without using an external network [134,144]. This is made possible, among other things, by the internet, which digitalizes the processes taking place and derives the ability to automate and widely share data [113]. In the above example of a closed energy cycle, the photovoltaic panels “inform” the charging station of the available power, which is then dispatched to the self-driving car. The vehicle, on the other hand, corrects the temperature of the battery according to power so that it can be charged as efficiently as possible. In addition, calculations and time simulations are routed to the mobile device to control the system.
In contrast, quick commerce creates a demand to reduce costs associated with numerous small delivery services [145]. This paves the way for autonomous vehicles that can communicate with their environment [146]. The emphasis on enhancing cybersecurity relates to small energy and information networks [147]. It is vital for maintaining supply chains, and as their numbers grow, it underscores the increasing reliance on digital solutions for further expansion [6,148].

3. Materials and Methods

A bibliometric study using the Scopus database was conducted to explore the influence and connections of logistics on the development of charging station networks and electric cars. This subject can be referred to as e-mobility or electromobility. This database was chosen due to the rigorous standards for scientific publications it indexes [149]. Scopus maintains strict quality and ethical criteria [150]. The selection of this research base is shaped by the obtained results, which are also trusted by other researchers. A detailed explanation of the research procedure is provided below, ensuring the potential for replication [151].
For this study, multiple search criteria targeting related content within the Scopus online database were employed, as detailed in Table 1. These search outcomes were subsequently utilized for a bibliometric analysis using the VOSviewer software [152,153]. VOSviewer is not the first or only bibliometric software to construct distance-based maps. However, due to VOSviewer’s mapping technique demonstrating strong performance compared to other methods, it can generate large-scale maps of multiple items in a co-citation map in a relatively short period of time [154]. The functionality of VOSviewer is particularly beneficial for displaying extensive bibliometric maps in an easy-to-understand manner [155,156]. It is likely the most popular science mapping tool, making it one of the most trusted bibliometric mapping tools used in science [157]. It is followed by CiteSpace and the Bibliometrix package for Biblioshiny [158].
Table 1 outlines the search criteria underpinning the subsequent bibliometric research [159]. Within the indexed content of the Scopus database, various document types exist, ranging from journal articles and conference papers to reviews and book chapters. From the entire database collection, 341 indexed publications were identified, spotlighted, and examined based on the Q1 query (refer to symbol Q1 in Table 2). Out of this collection, 21 publications were authored in Chinese. For these documents, available English abstracts were scrutinized.
In this paper, the Q1 query is introduced in relation to the paper’s title to validate it against contemporary research. The period mentioned, up to 2022, reflects the availability of publications from this span. Although new publications are emerging in the current year, 2023 has been excluded [10,160]. The extraction of results for query Q1, as presented in Table 2, was conducted on 10 April 2023. This timeline allowed for the indexing of the 2022 documents, ensuring the dataset’s comprehensiveness and reliability.
Query Q1 in Table 2 is designed to search for indexed titles, abstracts, and keywords within the Scopus online database collection. This intent is signaled by the initial segment of the query ‘TITLE-ABS-KEY’, positioned before the opening parenthesis of the query content. Within the parenthesis are terms related to logistics, charging stations, and electric vehicles (interpreted as electromobility). Given the varied terminology for charging stations in English, the terms ‘charging’, ‘station’, and ‘charger’ were employed. The asterisk in the query acts as a logical operator within the Scopus database. Furthermore, the operators used are displayed adjacent to the search keywords. The Q1 syntax utilizes the OR and AND operators, serving as conjunctions ‘or’ and ‘and’ to either amalgamate or filter out keywords in a search. This approach fosters more precise and efficient results. The query syntax also limits the search to publications up to 2022.
The results derived from the query in Table 2 were exported in the .ris file format. During the export process from the database, all selected publication fields were included [161]. Subsequent analyses of the gathered data were conducted using VOSviewer (version 1.6.19, Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands). This software facilitated the examination of the data procured from Q1. Generated maps included visualizations of network maps, overlap maps, keyword density maps, and cluster density maps [150,162]. To ensure the reproducibility of this study, the official VOSviewer manual version 1.6.19 was consulted during the preparation and description of the results [163].
VOSviewer displays results as a bibliometric map of the keyword co-occurrence network [164]. A network consists of elements and links between keywords (or other selected elements in the program). These items are grouped into clusters (sub-networks) and are automatically color-coded. In VOSviewer, clusters are mutually exclusive, meaning a keyword can belong to only one cluster. In this study, each cluster is assigned a number and a specific color. To scrutinize each generated map meticulously, the zoom and scroll functions of VOSviewer were employed [165]. Screenshots of this analysis are provided later in this paper. For other parameters pertinent to the visual representation of the results, such as scale, weights, and scores, default settings were retained. The sole exception was the graphical representation of results, where the size of individual keyword clusters was adjusted for clearer visualization.
This research does have its constraints. The chosen number of co-occurrences directly influences both the graphical presentation of the results and the clarity of the bibliometric map. As such, a baseline of 16 co-occurrences of keywords was established for each bibliometric map.

4. Results

This section draws from the extensive collection of results for Q1, as displayed in Table 2. The data was visualized using bibliometric maps in the VOSviewer software, covering indexed keyword co-occurrences, overlay analysis, keyword density, and cluster density. The order of the presented results is based on the research procedure in the VOSviewer program.
From the exploration of the Scopus database using query Q1, 341 publications were identified, spanning the years 1991 to 2022. Among the Q1 results, 256 publications indexed in Scopus were cited at least once. The relationship between cited and uncited publications is illustrated in Figure 2.
To analyze the results based on the queries presented in Table 2, the VOSviewer software was employed for co-occurrence analysis. This software creates bibliometric maps using bibliometric data and indexed content from the Scopus database. As such, Figure 3 is a bibliometric map generated in VOSviewer, employing the full counting method for co-occurrences of indexed keywords from the Scopus database, as derived from the Q1 query. This method identified a total of 2684 keywords. A high correlation rate was applied, and 16 co-occurrences of a keyword were selected, which then led to a score of 33 keywords. Excluding country names or companies, all 33 keywords were further examined in the VOSviewer software. This software automatically segmented the provided keywords into four groups (clusters), each delineated by a specific color. Figure 3 displays a map of the keywords most frequently used in scientific publications concerning logistics and electromobility.
Figure 3 displays four sub-networks of the bibliometric map in distinct colors, highlighting the automatically generated clusters. Keywords are depicted as nodes, with lines between them representing connections and the publications where these co-occurring keywords appear. Among all nodes, the keywords with the highest frequency of occurrence are: electric vehicles (178), charging (batteries) (136), electric vehicle (111), and vehicle routing (78). The largest nodes are positioned toward the inner side of the figure, indicating a higher edge density [166]. Wider lines suggest that multiple publications relate to the identified indexed keywords. The VOSviewer software automatically thematically linked the color-coded keywords. The red cluster pertains to electromobility, green represents mobility (transport), blue centers around planning, and yellow focuses on efficiency.
Table 3 presents the four thematic clusters of indexed keywords co-occurring in the bibliometric map shown in Figure 3. The most populous is the red cluster, consisting of 11 elements. Other significant clusters, marked in green and blue, contain 8 keywords each. The least populous is the yellow cluster, with 6 items detailed in Table 3. The elements outlined in Table 3 are identified by terms from the VOSviewer software. These indexed keywords are symbolized by the network nodes. Connecting these keywords are links, which represent the edges shown in the Figure 3 network. Edges symbolize co-occurrence links between terms, and their strength is noteworthy. Each map type displayed in this study contains only one kind of link, and in Figure 3, keyword co-occurrences are visualized.
In Table 3, the keywords within each cluster are arranged in alphabetical order, as determined by the VOSviewer software calculations. The number of occurrences for each keyword is provided in parentheses following the respective keyword.
The keywords in Table 3 are related to electromobility and the development of electric vehicle charging infrastructure although, the keywords, i.e., ‘electromobility’, ‘infrastructure development’, or ‘e-mobility’ were not explicitly mentioned by the VOSviewer program. This observation is confirmed by the fact that there is no electromobility without ‘electric vehicles’ and ‘charging stations’. The same applies to logistics, despite the fact that the keyword ‘logistics’ is used in a small number, while it is indicated again by the related keywords as ‘forecasting’, ‘transport’, ‘fleets’, or ‘transport planning’.
Using the results from Q1, the overlay map (Figure 4) and density maps (Figure 5 and Figure 6) were created. Figure 4 overlays indexed keywords from 2018 to 2021, representing a more specific period of evolution for the subjects of logistics and electromobility compared to the broader search spanning from 1991 to 2022. The legend at the bottom right corner of the visualization depicts the progression of research topics over time, illustrated through color mapping. The visualization of overlay keywords has a shape and node distribution similar to the visualization of the network of co-occurring keywords (as seen in Figure 3). Hence, a detailed descriptive table of keywords is not provided below Figure 4.
Figure 4 is a visualization of the development course of the indicated keyword usage in publications over time using the method of overlaying indexed co-occurrences of keywords. The visualization in Figure 3 is identical in general shape to Figure 3, with a distinction made between changes over time. These changes represent changes in the topic of the analyzed scientific papers. The calculation results presented in Figure 4 are the result of the VOSviewer algorithms and follow the full counting method of indexed keywords that met the condition of 16 common occurrences. The coverage analysis shows the oldest keywords related to logistics and e-mobility, represented by the darker blue color, and the newest keywords by the lighter colors green and yellow, respectively. The legend in Figure 4 shows the color change over the time of publication. Due to the increase in the number of publications in the 2018 range, it was the VOSviewer program that automatically determined the analyzed time frame. The darker, i.e., older keywords are mainly in the center of Figure 4; these include ‘logistics’ and ‘electric vehicles’. Then the green shades indicate ‘fleet operations’, ‘charging station’, and ‘costs’. The newest areas, marked in yellow, are ‘routing algorithms’, ‘vehicle-to-grid’, and ‘greenhouse gases’. A shift towards automation can be observed, as well as areas where huge amounts of data are being transformed, which may point towards further use of AI.
The density map visualization was carried out in two variants. The first is Figure 5, representing the item density visualization, and the second variant is Figure 6, indicating the cluster density visualization.
The keyword density visualization was generated in VOSviewer via the item density button in the options panel. In the density visualization in Figure 5, keywords are represented by labels, as in the network visualization. Each point (keyword) in Figure 5 is assigned a color that indicates the density of the given keywords. The default colors range from purple to green to yellow. The higher the number of keywords, the brighter the color, and in this case, more yellow indicates a higher number of occurrences.
It can be observed that the most significant keywords from this study are not among the most numerous clusters. The most numerous occurrences are electric vehicles and charging (batteries). The assignment of keywords automatically highlighted by VOSviewer to clusters allowed the analysis to continue as a cluster analysis.
To check the cluster density, the ‘Cluster density’ option in VOSviewer was selected, and Figure 6 was generated. The visualization of the cluster density shown is similar to the visualization of the position density (Figure 5), except that the position density is displayed separately for each cluster of clusters. In the cluster density visualization presented in Figure 6, the color of a point in the visualization is obtained by mixing the colors of different clusters.
The colors used automatically in the software are the same as those shown in Figure 5. In this step, the results of sub-networks oriented around the indicated keywords ‘charging station’, ‘logistics’, and ‘electric vehicles’ are presented against the entire network (Figure 6). The weight given to the color of a given cluster depends on the number of elements belonging to that cluster in the neighborhood of the point. As in the visualization of item density, the importance of the keyword is taken into account. In Figure 6, the yellow cluster, dedicated to efficiency and logistics, complements the other clusters.
Among the co-occurrence analysis results obtained (shown in Figure 3), significant relationships of keywords with other keywords such as ‘logistics’ (Figure 7), ‘charging station’ (Figure 8), and ‘charging infrastructures’ (Figure 9) were examined. Figure 7, Figure 8 and Figure 9 are the results of screenshots of dynamic analyses, i.e., situations where the mouse pointer was moved over a keyword in the VOSviewer main panel, where the network visualization is generated.
The dynamic keyword network analysis shown in Figure 7 presents a subnetwork of related keywords the position “logistics”. After ‘secondary batteries’, it is the second most numerous co-occurring item in the green cluster (Table 3). The keyword ‘logistics’ is also the Q1 item used to explore the Scopus database. This item has 30 links to other keywords, with a total link strength of 205, and has 46 co-occurrences in the explored results from the Scopus database.
The keyword shown in Figure 8 is ‘charging station’, and associated with it are 31 keywords. The colors of the nodes and edges are the same as for the clusters shown in Figure 3. The size of the element label and circle is dependent on the weight of the keyword and its importance. The greater the importance of the element, the larger the label and circle of the element. The visualization of the figure above consists of the links between the keywords associated with the ‘charging station’. The links between the individual keywords gradually change color. The links also show the distance between the nodes of the subnetwork with the central keyword ‘charging station’. This distance roughly indicates the affinity of the two keywords. The closer the two keywords co-occurred in publications, the closer the nodes were to each other and the stronger the affinity. In the figure above, there are 48 keywords related to the keyword ‘charging station’. This subnetwork has the same edges and nodes, gradually changing colors as shown in Figure 3. The item ‘charging station’ belonged to cluster 1 (Table 3) and has 31 links to other keywords, with a total link strength of 189, and has 48 co-occurrences in the explored results from the Scopus database.
The keyword ‘charging infrastructures’ represents the links of 24 keywords, as shown in Figure 9. The edges and nodes have the same colors as the clusters shown in Figure 3. The item ‘charging infrastructures’ belongs to the green cluster (Table 3) and has 29 links to other keywords, with a total link strength of 120, and has 24 co-occurrences in the explored results from the Scopus database.
The presented graphical analyses illustrate the complexity of aspects of logistics in electromobility and indicate the relevance of the topic of this thesis to the current situation in terms of multidisciplinary interrelationships among various market sectors. The results of the bibliometric analysis highlight the topicality of the importance of logistics in the development of the network of electric cars and charging stations in Europe [167] and point to further areas of analysis and comparative methods used by other researchers.
After conducting a dynamic analysis of the results from the queries in Table 2, specifically Q1, Q2, and Q3, we identified the 10 most cited articles. These were selected to offer a deeper insight into the intricate interrelationships between electromobility, energy, and logistics. These 10 articles stand out as some of the most influential in the literature on electromobility, the energy sector, and transportation. The most significant co-occurring keywords are those that are also predominantly found in these highly cited and influential works, boasting over 120 citations as presented in Table 4.
The articles in Table 4 were further analyzed. They predominantly address challenges related to energy, emphasizing the importance of ensuring a consistent energy supply for seamless logistics operations [171]. Topics include the strategic placement of charging stations [172] or hubs and ensuring adequate power for charging heavy-duty vehicles [174]. Additionally, these articles explore programming and automation aspects in selecting optimal routes [168,169], with the availability of recharging stations being a prominent research challenge in this domain [170,173].
The analysis of the most cited articles bears similarities to the bibliometric analysis conducted. The largest clusters in the figures represent the main research sectors, and their branches align with specific problems linking these sectors. An examination of these specific issues can be found in Table 4. Key among these are routing problems, optimizing locations where vehicles can charge, and investigating the potential of electromobility in specific areas. The most cited articles present analyses that align with the results from the bibliometric study.
Drawing insights from Table 4, Figure 10 in this article illustrates the various sectors and their interactions.
In the analysis conducted, the burgeoning prominence of electromobility integration into the logistics and energy sectors in contemporary scholarly discourse became evident. This trend is empirically supported by the high citation counts of the articles under examination, which attest to their significant influence on ensuing scholarly endeavors.
Prominently featured across these seminal works is the pivotal importance of energy, specifically the critical need to secure a robust energy supply to facilitate sustainable and efficient logistics operations [178]. Key areas of concern identified include the strategic establishment of charging infrastructure and ensuring adequate power supply, particularly for vehicles of higher tonnage [124]. These observations not only bring to light challenges in infrastructure but also signal wider implications for transitioning toward more environmentally friendly transport approaches [179].
Figure 10 visually captures the intricacies of the electromobility business ecosystem, shedding light on its complex interconnections and the specific issues being examined. This study identifies four main sectors: business ecosystem, energy sector, transport and logistics sector, and E-Mobility. Significantly, authors with the most influential citations are showcased in Figure 10, with annotations linking them to their particular thematic contributions. Challenges and their interrelations identified in the study are systematically grouped under each sector, providing a holistic view of the entire ecosystem.
Across the breadth of electromobility research, the fundamental role of infrastructure stands out, as vividly depicted in Figure 10. Key considerations, such as energy transmission systems, roadway designs, and charging solutions, are of paramount significance. While there are inherent variances across these, the need for well-thought-out infrastructure for the smooth functioning of the mentioned sectors is evident. The persistent emphasis on refining these infrastructures further highlights their academic significance.
A standout aspect in all sectors is the central importance of infrastructure, as clearly depicted in Figure 10. It’s essential to distinguish between energy transmission systems, roadway designs, and charging equipment. Despite their intrinsic differences, crafting purpose-built infrastructure remains critical for the effective operation of the sectors outlined. A closer look suggests that advancing and optimizing such infrastructures is a core area of attention in the referenced academic works.
In Table 5, outcomes from the classical literature review (CLR) are juxtaposed with those from the systematic literature review (SLR). Specifically, this table contrasts the topics discerned from bibliometric analysis with themes spotlighted in this study’s second sub-chapter, centered on megatrends and principal areas.
Table 5 provides a systematic comparison of primary themes from two distinct sources: the classical literature review and the systematic literature review aided by VOSviewer software. This comparison is designed to offer a comprehensive understanding of research areas in electromobility. The table is organized into three columns:
  • Areas/Themes: This column consolidates the central focus areas identified from both literature reviews.
  • Descriptions and Topics: Based on the classical literature review, this section outlines the specifics of each identified theme, detailing the academic trends, research focus, and inherent challenges of each area, giving a full perspective on the subject.
  • Cluster Keywords and Themes: Originating from the systematic literature review supported by VOSviewer software, this column lists grouped keywords and specific themes, providing a detailed view of the nuances within the broader themes presented in the first column.
A thorough examination of Table 5 underscores several salient observations:
  • Shared Themes: Electromobility’s significance is consistently highlighted across both literature reviews, particularly emphasizing the indispensable role of infrastructure, the nuances of route optimization, and the strategic considerations of energy supply.
  • Unique to the Classical Literature Review: This review accentuates the academic gravity of the discussed themes, as reflected by citation metrics. Furthermore, it offers a more expansive view of the overall business landscape of electromobility.
  • Unique to the Systematic Literature Review: With the aid of VOSviewer software, this review dives deeper into technical intricacies, spotlighting areas like freight and fleet dynamics, environmental considerations, and specific algorithmic methodologies.
  • Inferred Topics: Beyond explicitly stated themes, there are several underlying topics discernible within both texts. These inferred areas, while not directly highlighted, contribute significantly to the overarching discourse on electromobility.
In essence, Table 5 stands as a comprehensive analytical instrument, weaving together the insights of two specialized literature reviews to present an encompassing portrayal of the research terrain within electromobility. Its organized and detailed presentation ensures its position as a vital resource for researchers venturing into this evolving field.

5. Discussion

The academic community has extensively explored the technological intricacies of electromobility within the logistics sector. Optimization challenges stemming from advanced programming and automation, paired with recharging considerations, underscore the crucial interplay between technology and infrastructure in leveraging the full potential of electromobility in logistics.
New mobility models introduce market elements lacking standardization. Bibliometric map analyses reveal links between logistics and electromobility, yet they feature a wide range of terminology. This variability accentuates the importance of an all-encompassing interdisciplinary understanding. The term “electromobility”, often abbreviated as “e-mobility”, spans a vast range, encompassing electric vehicles, charging stations, and nascent systems like integrated charging networks. This broad perspective is reinforced by the array of keywords detected in the literature under review. An interesting observation is that slight terminological differences in Scopus database queries can lead to vast differences in article yields, as showcased in Table 2. Specific themes presented in Table 3 necessitate a profound understanding, both practical and theoretical. For instance, some studies might use ‘charging stations’ and ‘charging infrastructure’ synonymously.
Research predominantly emphasizes transportation, fleet management, and the conceptualization of a cohesive charging station network. These domains illuminate the growing relevance of electromobility in logistics and the broader business arena. As a burgeoning segment within transportation, e-mobility paves the way for innovative systems. Endeavors focusing on electric vehicles gain momentum from synergies with artificial intelligence advancements, addressing challenges from charging station networks to enhancing autonomous driving capabilities.
The evolution of electromobility is defined by its cross-sectoral interactions. Beyond mere technological intersections, cooperative ventures span domains from electricity provision to AI-orchestrated transport solutions. These collaborative endeavors, situated at the confluence of technological progression and emergent market demands, highlight electromobility’s transformative trajectory. They not only mark an industry’s advancement but also signify a paradigm shift in conceptualizing and operationalizing transportation and logistics in today’s interconnected age.
In summary, the literature on electromobility in logistics is both exhaustive and dynamic. While identifiable patterns emerge, the area remains in flux, shaped by technological innovations, market shifts, and interdisciplinary discussions. As electromobility progresses, continuous scholarly engagement is essential to ensure comprehensive and up-to-date understanding.
This research builds on discussions centered around the convergence of energy and transportation, illuminating key facets of electromobility in logistics. Topics include the role of advanced programming in optimization, the indispensable nature of infrastructure, and the plethora of terminologies within the electromobility sphere. The current literature is enhanced by these insights, further enriched by illustrative diagrams and tables. A detailed visualization underscores the symbiosis between technology and infrastructure in logistics, a critical factor in amplifying the benefits of electromobility (Figure 1). An in-depth analysis of the relationship between emerging mobility models and non-standardized market elements stresses the need for holistic interdisciplinary insight (Figure 2). Using the Scopus database, the study highlights variations in article outputs based on subtle terminology differences (Table 1). A thorough examination of trends, such as fleet management and charging station networks, provides a comprehensive view of the rapidly evolving electromobility landscape within logistics (Figure 3). This inquiry also reveals that integrating electromobility with AI advancements opens doors [180] to numerous innovative opportunities (Figure 4). The domain’s multiple intersections, ranging from electricity provision to AI-augmented transport solutions, emphasize the sector’s transformative potential (Figure 5). Ultimately, a conceptual model is presented, underscoring electromobility’s significance in the broader context of global energy transitions and sustainable mobility (Figure 6).
These findings offer valuable managerial insights. Rarely do academic pursuits navigate the intricate intersections of electromobility, logistics, and technology. This study identifies two principal paradigms. Initially, technological progress shapes logistical structures, particularly in the early phases of electromobility integration. Conversely, logistics requirements and market dynamics influence the course of technological innovations in later stages [158]. Such revelations imply that revising suboptimal strategies may necessitate technological recalibrations. Through in-depth scrutiny, this study conveys the potential ramifications of altering technological strategies for logistical operations.
The study’s objective is to furnish logistics experts and industry enthusiasts with insights to devise a path where electromobility technological strategies align with overarching sustainability and efficiency goals. The proposed framework accentuates the primacy of decisions rooted in rigorous research while acknowledging the continuously shifting electromobility terrain. This evidence-based methodology encourages organizations to explore a spectrum of solutions, aligning their logistical strategies with broader organizational aspirations.
The limitations of this research were the database and the reliability of the data. Scopus requires a standard academic subscription, which can be a source of limitations for those who would like to replicate this study. Next to accessibility is the structure of the data available to be exported from the database. There is a limitation of the used SLR variation method related directly to the query formulation. The main problem with the obtained results is the large number of synonyms and the variety of spellings for the matched results. On the level of the graphical representation, this research is limited by the choice of the number of co-occurrences, which determines the clarity of the bibliometric map.

6. Conclusions

The convergence of energy and transportation domains, spurred by electromobility’s ascent, is inducing transformative changes in logistics. These transitions capture not only broad systemic evolutions but also spotlight the intertwined dynamics of energy storage, distribution, and transportation. This study delves into the intricate ramifications of electric vehicles (EVs) and their auxiliary infrastructure on logistics, emphasizing the integrated nature of energy and transportation systems.
Key challenges arising from electromobility’s burgeoning role in logistics include the integration of IoT-driven automation, operational cost-effectiveness, and strategies to bolster EV utilization [181]. Within the European Union, these challenges are further nuanced by varied power standards, regional emission goals, and the delicate craft of route design, dependent on charging infrastructure. The accelerated adoption of advanced technologies, notably artificial intelligence (AI), is forging pathways for enhanced automation, notably impacting strategic planning and decision-making.
Electromobility represents a harbinger of change, reshaping the logistics and energy landscapes alike. This investigation highlights seminal contributions to the dialogue, focusing on primary obstacles and considerations. The deep-seated interconnections between electromobility, energy, and logistics emphasize the urgency of a coordinated research and deployment strategy. As technological and infrastructural advancements surge forward, future research is encouraged to build on these foundational insights, directing efforts towards sustainable solutions in logistics. By scrutinizing the business milieu surrounding electromobility, this study underscores adaptability’s quintessence. Such adaptability extends beyond mere technological progression, intertwining with existing societal, legal, and environmental contexts. The article argues that the triumph of electromobility is not predicated solely on technological milestones but thrives in a dynamic business environment characterized by adaptability, mutual innovation, and cohesive partnerships. This interdisciplinary approach is very necessary to investigate electromobility, and observing logistics and business ecosystems adds real value to the existing body of knowledge in this domain. Through a sustainability prism, this underscores the alignment of the ecosystem with shared objectives, mutual benefit realization, and its intricate interplay with myriad stakeholders.
While this study offers a comprehensive view of the current landscape, the feedback on the significance of stakeholders’ perspectives in the transportation and energy sectors is duly noted [182,183]. In future iterations or expansions of this research, it would be prudent to conduct an enriched literature review—both classical and systematic—emphasizing the role of stakeholders in electromobility [133]. The term ‘stakeholder’ will be pivotal in this context, helping to unearth deeper insights and potentially drawing new conclusions from the classical and bibliometric analyses. The dynamism of the transportation and energy sectors necessitates continuous updating and refining of our understanding, particularly as it pertains to stakeholders [184,185] who have the capacity to shape and direct the trajectory of advancements [138,186].
In summary, this research provides an in-depth analysis of the logistical shifts triggered by electromobility, elucidating its business ecosystem and emphasizing collaborative strategies, intrinsic challenges, and burgeoning prospects that define this rapidly evolving domain. By doing so, it invites stakeholders to perceive e-mobility as an integral part of the broader narrative of global energy transitions and sustainable transportation.
Prospective research endeavors should not be confined to solely technical and infrastructural challenges. Instead, they must embrace a broader purview, probing the vast socio-economic implications of incorporating electromobility into the broader logistics and energy landscapes. A truly comprehensive perspective is essential to pave the way for a sustainable and efficient future in transportation and energy.

Author Contributions

Conceptualization, A.S. and S.S.G.; methodology, A.S. and S.S.G.; formal analysis, A.S. and S.S.G.; investigation, A.S. and S.S.G.; writing—original draft preparation, A.S. and S.S.G.; writing—review and editing, A.S. and S.S.G.; visualization, A.S. and S.S.G.; supervision, A.S.; project administration, A.S. and S.S.G.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.

Funding

(A.S.) The project is financed by the National Science Centre in Poland under the program “Business Ecosystem of the Environmental Goods and Services Sector in Poland”, implemented in 2020–2022; project number 2019/33/N/HS4/02957; total funding amount PLN 120,900.00.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank Agnieszka Dramińska from Wroclaw University of Economics and Business Library for providing the Scopus database training.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study, the collection, analysis, or interpretation of data, the writing of the manuscript, or the decision to publish the results.

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Figure 1. Logistics Trend Radar version 6.0 by DHL (Copyright Creative Commons Attribution (CC BY). Source: Authors’ elaboration based on [84].
Figure 1. Logistics Trend Radar version 6.0 by DHL (Copyright Creative Commons Attribution (CC BY). Source: Authors’ elaboration based on [84].
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Energies 16 07249 g002
Figure 2. Ratio of cited to un-cited publications. Source: Authors’ elaboration.
Figure 2. Ratio of cited to un-cited publications. Source: Authors’ elaboration.
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Energies 16 07249 g003
Figure 3. Indexed keywords co-occurrence map;. Results of Q1 analyzed with full counting method. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
Figure 3. Indexed keywords co-occurrence map;. Results of Q1 analyzed with full counting method. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
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Energies 16 07249 g004
Figure 4. Overlay map of indexed co-occurrences. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
Figure 4. Overlay map of indexed co-occurrences. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
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Energies 16 07249 g005
Figure 5. Density visualisation map. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
Figure 5. Density visualisation map. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
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Energies 16 07249 g006
Figure 6. Cluster density visualization map. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
Figure 6. Cluster density visualization map. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
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Energies 16 07249 g007
Figure 7. Relationship of the keyword ‘logistics’ with other keywords. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
Figure 7. Relationship of the keyword ‘logistics’ with other keywords. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
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Energies 16 07249 g008
Figure 8. Relationship of the keyword ‘charging station’ with other keywords. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
Figure 8. Relationship of the keyword ‘charging station’ with other keywords. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
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Energies 16 07249 g009
Figure 9. Relationship of the keyword ‘charging infrastructures’ with other keywords. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
Figure 9. Relationship of the keyword ‘charging infrastructures’ with other keywords. Source: Authors’ elaboration in VOSviewer (version 1.6.19).
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Energies 16 07249 g010
Figure 10. Mapping the Research Landscape: Key Focus Areas from the 10 Most-Cited Papers Source: Authors’ elaboration based on [168,169,170,171,172,173,174,175,176,177].
Figure 10. Mapping the Research Landscape: Key Focus Areas from the 10 Most-Cited Papers Source: Authors’ elaboration based on [168,169,170,171,172,173,174,175,176,177].
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Table 1. Scopus database search criteria.
Table 1. Scopus database search criteria.
CriteriaDetails
DatabaseScopus
Search areaArticle title *, Abstract, Keywords
TopicsLogistics and electromobility
Time span1991–2022
Subject areaEngineering (221), Computer Science (123), Energy (92), Mathematics (87), Social Sciences (80), Environmental Science (49), Decision Sciences (47), Business, Management and Accounting (40), Physics and Astronomy (18), Economics, Econometrics and Finance (11), Materials Science (10)
Document typeArticle (197), Conference paper (108), Conference review (14), Book chapter (11), Review (7), Book (3), Erratum (1)
LanguageAny language; English (320) and Chinese (21)
Publication stagePublished (338), Media article (3)
* Each publication title is indexed in the Scopus database. Source: Authors’ elaboration.
Table 2. Search query syntax details.
Table 2. Search query syntax details.
SymbolQuery SyntaxNo. Results
(10 April 2023)
Q1TITLE-ABS-KEY (“charging” OR “station*” OR “charger *” AND “logistic *” AND “electric vehicle *”) AND PUBYEAR > 1991 AND PUBYEAR < 2023341
Q2TITLE-ABS-KEY (“charging” OR “station *” OR “charger *” AND “logistic *” AND “Electric Vehicle *” AND “electromobility” OR “e*mobility”) AND PUBYEAR > 1991 AND PUBYEAR < 20237
Q3ALL (“charging” OR “station *” OR “charger *” AND “logistic *” AND “electric vehicle *” AND “business ecosystem *”) AND (EXCLUDE (PUBYEAR, 2023))32
* Each publication title is indexed in the Scopus database. Source: Authors’ elaboration.
Table 3. Identified clusters of keyword co-occurrences visible in Figure 3.
Table 3. Identified clusters of keyword co-occurrences visible in Figure 3.
ClusterColorKeywords
1Redcharging batteries (136), charging station (48), decision making (16), electric automobiles (41), electric power transmission network (25), electric vehicle (111), electric vehicles (178), electric vehicles (evs) (31), forecasting (17), logistic regression (27), vehicle-to-grid (23)
2Greencharging infrastructures (24), fleet operations (46), freight transportation (16), greenhouse gases (24), location (18), logistics (46), secondary batteries (59), trucks (19)
3Blueenergy utilization (19), optimization (33), routing algorithms (18), vehicle routing (78), vehicle routing problem (18), vehicle routing problem with time windows (18), vehicle routing problems (23), vehicles (41)
4Yellowcharging time (17), commercial vehicles (24), costs (25), green logistics (22), integer programming (43), routing (16)
Source: Authors’ elaboration.
Table 4. Top 10 most cited articles among those analyzed.
Table 4. Top 10 most cited articles among those analyzed.
No.TitleAuthorsCitations
(1 September 2023)
1The electric vehicle-routing problem with time windows and recharging stations Schneider M., Stenger A., Goeke D. 698
2Routing a mixed fleet of electric and conventional vehiclesGoeke D., Schneider M. 351
3Battery swap station location-routing problem with capacitated electric vehiclesYang J., Sun H. 260
4Electric vehicle route optimization considering time-of-use electricity price by learnable partheno-genetic algorithmYang H., Yang S., Xu Y., Cao E., Lai M., Dong Z. 192
5The electric location routing problem with time windows and partial rechargingSchiffer M., Walther G. 186
6Evaluating the use of an urban consolidation centre and electric vehicles in central LondonBrowne M., Allen J., Leonardi J. 157
7The potential of electric trucks—An international commodity-level analysisLiimatainen H., van Vliet O., Aplyn D. 126
8Analyzing consumer attitudes towards electric vehicle purchasing intentions in Spain: Technological limitations and vehicle confidenceJunquera B., Moreno B., Álvarez R. 126
9A comprehensive model of regional electric vehicle adoption and penetrationJavid R.J., Nejat A. 122
10Electric vehicles in logistics and transportation: A survey on emerging environmental, strategic, and operational challengesJuan A.A., Mendez C.A., Faulin J., De Armas J., Grasman S.E. 122
Source: Authors’ elaboration based on Scopus database search results [168,169,170,171,172,173,174,175,176,177].
Table 5. Comparison of CLR vs. SLR Outcomes.
Table 5. Comparison of CLR vs. SLR Outcomes.
Areas/ThemesClassical Literature Review (CLR)Systematic Literature Review (SLR)
Electromobility and
Infrastructure
-
Increasing prominence in research
-
High citation counts
-
Importance of consistent energy for logistics
-
Electric Vehicle Infrastructure and Technologies
-
Electric Vehicle Charging Infrastructure
-
Charging and Cost Concerns
Strategic Charging
Infrastructure
-
Importance of strategic charging hubs
-
Ensuring power for charging
-
Fit-for-purpose infrastructure
-
Electric Vehicle Charging Infrastructure
-
Charging and Cost Concerns
Route Optimization and Automation
-
Emphasis on programming and automation
-
Recharging station placement
-
Electric vehicle-routing problems
-
Routing and Logistics Optimization
Energy Considerations in Electromobility
-
Challenges related to energy
-
Robust energy supply
-
Transitioning to eco-friendly transport
-
Electric Vehicle Infrastructure and Technologies
Consumer Attitudes and Vehicle Adoption
-
Consumer perceptions towards EVs
-
Adoption rate of EVs
-
No direct equivalent from SLR, but could infer from environmental concerns
Business Ecosystem and Research Landscape
-
Electromobility business ecosystem
-
Interconnections and issues
-
Authors with high citation impacts
-
No direct equivalent in SLR method results
Freight and Fleet
Operations
-
Not directly mentioned in CLR method results, but inferred from logistics operations
-
Freight and Fleet Operations
Environmental Concerns and Green Logistics
-
Could be inferred from transitioning to eco-friendly transport methods
-
Environmental Concerns
-
Vehicle Types and Green Logistics
Mathematical Approaches
-
Could be inferred from emphasis on programming and automation
-
Mathematical Models
Source: Authors’ elaboration.
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Grzesiak, S.S.; Sulich, A. Electromobility: Logistics and Business Ecosystem Perspectives Review. Energies 2023, 16, 7249. https://doi.org/10.3390/en16217249

AMA Style

Grzesiak SS, Sulich A. Electromobility: Logistics and Business Ecosystem Perspectives Review. Energies. 2023; 16(21):7249. https://doi.org/10.3390/en16217249

Chicago/Turabian Style

Grzesiak, Sebastian Szymon, and Adam Sulich. 2023. "Electromobility: Logistics and Business Ecosystem Perspectives Review" Energies 16, no. 21: 7249. https://doi.org/10.3390/en16217249

APA Style

Grzesiak, S. S., & Sulich, A. (2023). Electromobility: Logistics and Business Ecosystem Perspectives Review. Energies, 16(21), 7249. https://doi.org/10.3390/en16217249

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