Quantifying System Level Impact of Connected and Automated Vehicles in an Urban Corridor
2022-01-0173
03/29/2022
- Event
- Content
- Numerous studies have demonstrated significant energy reduction for an ego vehicle by up to 20% leveraging Vehicle-to-Everything (V2X) technologies [1, 2, 3, 4]. Some studies have also analyzed the impact of such vehicles on the energy consumption of other vehicles in a suburban or a highway corridor [5, 6]. Southwest Research Institute (SwRI), in collaboration with Continental and Hyundai, is currently working on a Department of Energy funded project that is focused on quantifying the impact of multiple ego vehicles (smart vehicles) on the total energy consumption of the corridor under various traffic conditions, vehicle electrification level, vehicle-to-vehicle (V2V) technology penetration, and the number of smart (ego) vehicles in an urban setting. A six-kilometer-long urban corridor from Columbus, Ohio was modeled and calibrated via real-world data in PTV Vissim traffic microsimulation software. Five forward-looking powertrain models, consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles, were developed to estimate the energy consumption of vehicles on the corridor. A comprehensive full factorial simulation study was performed. The preliminary simulation results indicate that for a traffic mix based on the new vehicles sales in 2025, a 15% corridor-level energy consumption reduction can be achieved. The paper details the development and validation of the simulation framework, design of experiments conducted, a discussion of challenges faced and results under various test conditions. References: 1. Olin, P., Aggoune, K., Tang, L., Confer, K. et al., "Reducing Fuel Consumption by Using Information from Connected and Automated Vehicle Modules to Optimize Propulsion System Control," SAE Technical Paper 2019-01-1213, 2019, https://doi.org/10.4271/2019-01-1213. 2. Oncken, J., Orlando, J., Bhat, P., Narodzonek, B. et al., "A Connected Controls and Optimization System for Vehicle Dynamics and Powertrain Operation on a Light-Duty Plug-In Multi-Mode Hybrid Electric Vehicle," SAE Technical Paper 2020-01-0591, 2020, https://doi.org/10.4271/2020-01-0591. 3. Rengarajan, S., Hotz, S., Sarlashkar, J., Gankov, S. et al., "Energy Efficient Maneuvering of Connected and Automated Vehicles," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(6):3231-3239, 2020, https://doi.org/10.4271/2020-01-0583. 4. Asher, Z., Patil, A., Wifvat, V., Frank, A. et al., "Identification and Review of the Research Gaps Preventing a Realization of Optimal Energy Management Strategies in Vehicles," SAE Int. J. Alt. Power. 8(2):133-149, 2019, https://doi.org/10.4271/08-08-02-0009. 5. Nianfeng Wan, Ardalan Vahidi, Andre Luckow, Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic, Transportation Research Part C: Emerging Technologies, Volume 69, 2016, Pages 548-563, ISSN 0968-090X, https://doi.org/10.1016/j.trc.2016.01.011. 6. Ard, Tyler, Dollar, Robert Austin, Vahidi, Ardalan, Zhang, Yaozhong, and Karbowski, Dominik. Microsimulation of energy and flow effects from optimal automated driving in mixed traffic. United Kingdom: N. p., 2020. Web. doi:10.1016/j.trc.2020.102806.
- Citation
- Bhagdikar, P., Gankov, S., Rengarajan, S., Sarlashkar, J. et al., "Quantifying System Level Impact of Connected and Automated Vehicles in an Urban Corridor," SAE Technical Paper 2022-01-0173, 2022, .