How Connectivity: CV, CAV, and V2X affects Energy and Environment

Connected autonomous vehicles (CAVs) are expected to optimize energy efficiency due to improved operational efficiencies and by moderating movements of automated vehicles (AVs) through Cooperative Adaptive Cruise Control (CACC), platooning, eco-driving strategies, Vehicle-to-Everything (V2X) communication and incorporation of various dynamic routing systems [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. For example, Djavadian et al., [16] proposed a dynamic multi-objective eco-routing strategy for connected & automated vehicles (CAVs) and implemented in a distributed traffic management system which shows the potential of reducing GHG and NOx emissions by 43 percent and 18.58 percent, respectively. Similarly, the eco-drive system for connected and automated vehicles proposed by Ma et al., [17] shows that more than 20 percent of fuel consumption can be saved. Mattas et al., [147] shows that while AVs lacking interconnectivity would likely increase emissions, a network of CAVs could lead to a decrease in carbon dioxide emissions of up to 5 percent.

V2X technology has potential to improve energy efficiency through applications such as traffic-light-to-vehicle communication, which can create energy savings and increased driving range [18]. However, vehicular communication systems also require infrastructure and energy to support [19]. Additional research is needed to understand potential environmental impacts of V2X technology, and whether there will be a net benefit when it comes to energy efficiency.

References

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  2. L. C. Bento, R. Parafita, H. A. Rakha, and U. J. Nunes, “A study of the environmental impacts of intelligent automated vehicle control at intersections via V2V and V2I communications,” J. Intell. Transp. Syst., vol. 23, no. 1, pp. 41–59, Jan. 2019, doi: 10.1080/15472450.2018.1501272.

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  6. R. Tu, L. Alfaseeh, S. Djavadian, B. Farooq, and M. Hatzopoulou, “Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO2 concentrations,” Transp. Res. Part Transp. Environ., vol. 73, pp. 142–151, Aug. 2019, doi: 10.1016/j.trd.2019.06.008.

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  8. H. Tu, L. Zhao, R. Tu, and H. Li, “The energy-saving effect of early-stage autonomous vehicles: A case study and recommendations in a metropolitan area,” Energy, vol. 297, p. 131274, Jun. 2024, doi: 10.1016/j.energy.2024.131274.

  9. M. Lokhandwala and H. Cai, “Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC,” Transp. Res. Part C Emerg. Technol., vol. 97, pp. 45–60, Dec. 2018, doi: 10.1016/j.trc.2018.10.007.

  10. H. Zhang, C. J. R. Sheppard, T. E. Lipman, T. Zeng, and S. J. Moura, “Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas,” Transp. Res. Part Transp. Environ., vol. 78, p. 102210, Jan. 2020, doi: 10.1016/j.trd.2019.102210.

  11. H. Miao, H. Jia, J. Li, and T. Z. Qiu, “Autonomous connected electric vehicle (ACEV)-based car-sharing system modeling and optimal planning: A unified two-stage multi-objective optimization methodology,” Energy, vol. 169, pp. 797–818, Feb. 2019, doi: 10.1016/j.energy.2018.12.066.

  12. E. C. Jones and B. D. Leibowicz, “Contributions of shared autonomous vehicles to climate change mitigation,” Transp. Res. Part Transp. Environ., vol. 72, pp. 279–298, Jul. 2019, doi: 10.1016/j.trd.2019.05.005.

  13. F. Yao, J. Zhu, J. Yu, C. Chen, and X. (Michael) Chen, “Hybrid operations of human driving vehicles and automated vehicles with data-driven agent-based simulation,” Transp. Res. Part Transp. Environ., vol. 86, p. 102469, Sep. 2020, doi: 10.1016/j.trd.2020.102469.

  14. T. D. Chen, K. M. Kockelman, and J. P. Hanna, “Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions,” Transp. Res. Part Policy Pract., vol. 94, pp. 243–254, Dec. 2016, doi: 10.1016/j.tra.2016.08.020.

  15. J. H. Gawron, G. A. Keoleian, R. D. De Kleine, T. J. Wallington, and H. C. Kim, “Deep decarbonization from electrified autonomous taxi fleets: Life cycle assessment and case study in Austin, TX,” Transp. Res. Part Transp. Environ., vol. 73, pp. 130–141, Aug. 2019, doi: 10.1016/j.trd.2019.06.007.

  16. S. Djavadian, R. Tu, B. Farooq, and M. Hatzopoulou, “Multi-objective eco-routing for dynamic control of connected & automated vehicles,” Transp. Res. Part Transp. Environ., vol. 87, p. 102513, Oct. 2020, doi: 10.1016/j.trd.2020.102513.

  17. J. Ma, J. Hu, E. Leslie, F. Zhou, P. Huang, and J. Bared, “An eco-drive experiment on rolling terrains for fuel consumption optimization with connected automated vehicles,” Transp. Res. Part C Emerg. Technol., vol. 100, pp. 125–141, Mar. 2019, doi: 10.1016/j.trc.2019.01.010.

  18. T. Tielert, D. Rieger, H. Hartenstein, R. Luz, and S. Hausberger, “Can V2X communication help electric vehicles save energy?,” in 2012 12th International Conference on ITS Telecommunications, Nov. 2012, pp. 232–237. doi: 10.1109/ITST.2012.6425172.

  19. M. Georgiades and M. S. Poullas, “Emerging Technologies for V2X Communication and Vehicular Edge Computing in the 6G era: Challenges and Opportunities for Sustainable IoV,” in 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Pafos, Cyprus: IEEE, Jun. 2023, pp. 684–693. doi: 10.1109/DCOSS-IoT58021.2023.00108.

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Note: Mobility COE research partners conducted this literature review in Spring of 2024 based on research available at the time. Unless otherwise noted, this content has not been updated to reflect newer research.

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