Demonstration of energy consumption reduction in class 8 trucks using eco-driving algorithm based on on-road testing
2022-01-0157
03/29/2022
- Event
- Content
- Vehicle to Everything (V2X) communication has enabled on-board access to the information from other vehicles and infrastructure. This information, traditionally used for safety applications, is increasingly being used for improving fuel economy of the vehicles [1, 2, 3, 4, 5]. This work aims at demonstrating energy consumption reduction in heavy/medium duty vehicles using the eco-driving algorithm. The algorithm is enabled by V2X communication and uses data contained in basic safety messages (BSMs) and signal phase and timing (SPaT) to generate an energy-efficient velocity trajectory for the vehicle to follow. A ~25.7 km (16-mi) urban corridor in Bexar County, Texas, was modeled in a microscopic traffic simulation package and calibrated to match real-world conditions. For the trucks using this algorithm, a nominal reduction of 7% in energy consumption and 6% in trip time was observed in simulation. This was validated by on-track testing of a pair of velocity profiles. The track testing procedure was based on the SAE J1321 recommended practice [6]. Some modifications were made to the procedure based on program objectives and constraints. A fuel consumption reduction of 6.83% ± 4.61% was observed based on the track testing of the pair of velocity traces. Before testing the velocity traces generated using the traffic simulation, the team went through an exercise to understand the achievable upper bound on energy consumption benefits based on a drive cycle synthesized by National Renewable Energy Laboratory (NREL) for Port Drayage application [7]. References: [1] "Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR)," ARPA-E, [Online]. Available: https://arpa-e.energy.gov/technologies/programs/nextcar. [Accessed 2021]. [2] "IMplementation of Powertrain Control for Economic and Clean Real driving emIssion and fuel ConsUMption," [Online]. Available: https://ec.europa.eu/inea/en/printpdf/3516. [Accessed 2021]. [3] J. Guanetti, Y. Kim and F. Borrelli, "Control of Connected and Automated Vehicles: State of the Art and Future Challenges," Annual Reviews in Control, vol. 45, pp. 18-40, 2018. [4] D. Shen, D. Karbowski and A. Rousseau, "Fuel Efficient Speed Optimization for Real-World Highway Cruising," in SAE Technical Paper 2018-01-0589, 2018. [5] Z. Wadud, D. MacKenzie and P. Leiby, "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part A: Policy and Practice, vol. 86, pp. 1-18, 2016. [6] Committee, Truck and Bus Aerodynamics and Fuel Economy, "Fuel Consumption Test Procedure - Type II," SAE International, 2020. [Online]. Available: https://doi.org/10.4271/J1321_202010. [Accessed 2021]. [7] "NREL DriveCAT - Chassis Dynamometer Drive Cycles," National Renewable Energy Laboratory, [Online]. Available: www.nrel.gov/transportation/drive-cycle-tool. [Accessed 2021].
- Citation
- Bhagdikar, P., Gankov, S., Frazier, C., Rengarajan, S. et al., "Demonstration of energy consumption reduction in class 8 trucks using eco-driving algorithm based on on-road testing," SAE Technical Paper 2022-01-0157, 2022, .