A Graph-Theoretic Approach for Exploring the Relationship Between EV Adoption and Charging Infrastructure Growth
Abstract
:1. Introduction
2. Literature Review
2.1. Impact of Charging Infrastructure and Other Factors on EV Adoption
2.2. Application of Graph Model to Charging Infrastructure
2.3. Gaps Found in the Literature
- Most of the existing literature examines EV adoption and its relationship with other factors in one to three cities, resulting in limited data diversity. This study, however, analyzes 137 counties across six U.S. states, enhancing the analysis and demonstrating the role of states in clustering counties within the correlation networks.
- The use of graph models has mostly been limited to optimizing the placement of charging stations within specific areas. In contrast, this study contributes by applying a graph model to explore patterns in both EV adoption and CI growth networks under two scenarios of Early Adoption and Late Adoption, build a correlation network, and cluster counties accordingly.
- Existing studies rarely examine how the relationship between EV adoption and CI growth varies across different adoption scenarios (e.g., Early Adoption vs. Late Adoption) or under varying temporal granularities and lags. This limits our understanding of the timing and directionality of influence between the two. Our study addresses this by incorporating multiple time granularities and adoption phases to uncover nuanced structural and causal patterns.
3. Methodology
3.1. Data Collection
3.1.1. EV Data
3.1.2. CI Data
3.2. Proposed Graph-Theoretic Approach: Correlation Network
3.3. Early Versus Late Adoption
3.3.1. Early Adoption
3.3.2. Late Adoption
3.4. Different Time Granularities
3.5. Causality Relationships Between EVs and CIs
- Early Adoption Scenario: This hypothesis tests whether the growth of EV adoption leads to subsequent growth in CI. The null hypothesis is the following: EV adoption growth does not Granger-cause CI growth.
- Late Adoption Scenario: This hypothesis tests whether the growth of CI drives EV adoption. The null hypothesis is the following: CI growth does not Granger-cause EV adoption.
4. Results
4.1. Early Adoption of EV
4.2. Late Adoption of EV
4.3. Causal Relationships Between EV Adoption and CI Growth
5. Limitations and Future Work
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No | State | # of Completed Counties |
---|---|---|
1 | Colorado | 20 |
2 | Minnesota | 3 |
3 | Montana | 2 |
4 | New York | 48 |
6 | Texas | 30 |
7 | Virginia | 34 |
8 | Total | 137 |
Granularity | EV vs. DC | EV vs. Level 2 | EV vs. ALL |
---|---|---|---|
Monthly | 0.1772 | 0.2205 | 0.2305 |
Bi-monthly | 0.0972 | 0.1345 | 0.1416 |
Quarterly | 0.0715 | 0.0624 | 0.0543 |
Bi-annually | 0.0123 | 0.0366 | 0.0214 |
Granularity | EV vs. DC | EV vs. Level 2 | EV vs. ALL |
---|---|---|---|
Monthly | 0.1415 | 0.2529 | 0.2667 |
Bi-monthly | 0.1806 | 0.1347 | 0.1824 |
Quarterly | 0.0897 | 0.0737 | 0.0619 |
Bi-annually | 0.0263 | 0.0368 | 0.0203 |
Granularity\Lag | Half-Year | One-Year | Two-Year | Three-Year | Unique |
---|---|---|---|---|---|
monthly | 12 | 20 | 16 | 13 | 49 |
Bi-monthly | 12 | 16 | 18 | 14 | 52 |
quarterly | 17 | 19 | 17 | 15 | 57 |
Bi-annual | 9 | 12 | 6 | 15 | 30 |
Unique | 28 | 38 | 45 | 33 | 93 |
Granularity\Lag | Half-Year | One-Year | Two-Year | Three-Year | Unique |
---|---|---|---|---|---|
monthly | 18 | 11 | 9 | 9 | 37 |
Bi-monthly | 23 | 9 | 4 | 12 | 39 |
quarterly | 15 | 6 | 4 | 8 | 27 |
Bi-annual | 17 | 8 | 7 | 8 | 35 |
Unique | 44 | 26 | 20 | 26 | 77 |
State | EV → ALL | ALL → EV | Unique |
---|---|---|---|
New York | 36 | 26 | 40 |
Texas | 15 | 23 | 26 |
Virginia | 23 | 16 | 27 |
Colorado | 16 | 8 | 18 |
Minnesota | 2 | 2 | 2 |
Montana | 1 | 2 | 2 |
Unique | 93 | 77 | 115 |
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Alrasheedi, F.S.; Ali, H.H. A Graph-Theoretic Approach for Exploring the Relationship Between EV Adoption and Charging Infrastructure Growth. Vehicles 2025, 7, 54. https://doi.org/10.3390/vehicles7020054
Alrasheedi FS, Ali HH. A Graph-Theoretic Approach for Exploring the Relationship Between EV Adoption and Charging Infrastructure Growth. Vehicles. 2025; 7(2):54. https://doi.org/10.3390/vehicles7020054
Chicago/Turabian StyleAlrasheedi, Fahad S., and Hesham H. Ali. 2025. "A Graph-Theoretic Approach for Exploring the Relationship Between EV Adoption and Charging Infrastructure Growth" Vehicles 7, no. 2: 54. https://doi.org/10.3390/vehicles7020054
APA StyleAlrasheedi, F. S., & Ali, H. H. (2025). A Graph-Theoretic Approach for Exploring the Relationship Between EV Adoption and Charging Infrastructure Growth. Vehicles, 7(2), 54. https://doi.org/10.3390/vehicles7020054