Simulation based method for the analysis of energy-efficient driving algorithms using SUMO
DOI:
https://doi.org/10.52825/scp.v2i.91Abstract
The limited possibilities to evaluate the energy efficiency of driving algorithms for connected and autonomous vehicles (CAVs) make it very difficult for policymakers to decide on the potential of autonomous driving. This study is introducing a method to analyze the energy performance of a driving algorithm under various simulated traffic conditions using the microscopic traffic simulator SUMO. The method can also be used to optimize driving algorithm parameters for chosen traffic scenarios. Therefore, a tool-chain is developed that can simulate a CAV under many traffic scenarios in SUMO systematically. In those scenarios, one or more vehicles are controlled by the implemented driving algorithm. The resulting driving cycles are then analyzed by a forward-facing energy model to calculate the consumed energy. To validate the model, three measurement cycles under real urban traffic conditions were taken and the speed and state of charge (SOC) data of the test vehicle, a 2017 Tesla Model S 75D, were collected. The energy model was shown to be highly accurate and the simulated road network and traffic, which were chosen to represent the same urban traffic scenario as the measured cycles, were shown to result in similar statistics as the measurements. A simple driving algorithm that is already implemented in SUMO’s Kraus car-following model was chosen to verify the model’s applicability. For different values of the algorithm parameters acceleration and deceleration, a range of random driving cycles was simulated. In the simulations and the measurements, the effect of higher and lower use of auxiliary systems was also analyzed. The results show that the analyzed driving algorithm achieves similar results for the energy consumption as the human driver in the measurements with the best performing parameters. Also, the significance of auxiliary system use on the energy consumption and its effect on a driving algorithm’s parameter to remain energy efficient due to the higher impact of the trip duration is pointed out.
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References
[1] Cyberbotics Ltd. Webots.
[2] Geschäftsstelle der Teststrecke für automatisiertes und vernetztes Fahren Hamburg c/o ITS mobility e. V. Tavf streckenkarte. https://tavf.hamburg/fileadmin/user_upload/Bilder/TAVF_Streckenkarte_quer_de_200108.jpg. Last accessed on 2021-05-16.
[3] Chiara Fiori, Kyoungho Ahn, and Hesham A Rakha. Power-based electric vehicle energy consumption model: Model development and validation. Applied Energy, 168:257–268, 2016.
[4] Ray Galvin. Energy consumption effects of speed and acceleration in electric vehicles: Laboratory case studies and implications for drivers and policymakers. Transportation Research Part D: Transport and Environment, 53:234–248, 2017.
[5] Geschäftsstelle der Teststrecke für automatisiertes und vernetztes Fahren Hamburg c/o ITS mobility e. V. Teststrecke für automatisiertes und vernetztes fahren in hamburg. https://tavf.hamburg.
[6] Jihun Han, Antonio Sciarretta, Luis Leon Ojeda, Giovanni De Nunzio, and Laurent Thibault. Safe-and eco-driving control for connected and automated electric vehicles using analytical stateconstrained optimal solution. IEEE Transactions on Intelligent Vehicles, 3(2):163–172, 2018.
[7] HMS Industrial Networks. cananalyzer mini.
[8] IPETRONIK GmbH & Co. KG, Parsevalstraße 9a. SAM-CAN-ISO011.
[9] Stefan Krauß. Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics. 1998.
[10] Tamás Kurczveil, Pablo Álvarez López, and Eckehard Schnieder. Implementation of an energy model and a charging infrastructure in sumo. In Simulation of Urban MObility User Conference, pages 33–43. Springer, 2013.
[11] Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wießner. Microscopic traffic simulation using sumo. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2575–2582. IEEE, 2018.
[12] Blaž Luin, Stojan Petelin, and Fouad Al-Mansour. Microsimulation of electric vehicle energy consumption. Energy, 174:24–32, 2019.
[13] Tony Markel, Aaron Brooker, Terry Hendricks, Valerie Johnson, Kenneth Kelly, Bill Kramer, Michael O’Keefe, Sam Sprik, and Keith Wipke. Advisor: a systems analysis tool for advanced vehicle modeling. Journal of power sources, 110(2):255–266, 2002.
[14] The Mathworks, Inc., Natick, Massachusetts. MATLAB version 9.9.0.1467703 (R2020b), 2020.
[15] Christopher Oellerich and Rasmus Rettig. Entwicklung eines modells zur simulation der energiebilanz beim betrieb eines elektrofahrzeugs, 2020. unpublished.
[16] Travis E Oliphant. A guide to NumPy, volume 1. Trelgol Publishing USA, 2006.
[17] Tesla Model S 75D. Tesla Model S characteristics, 2017.
[18] Guido Van Rossum et al. Python programming language., 2007.
[19] Axel Wegener, Micha l Piórkowski, Maxim Raya, Horst Hellbrück, Stefan Fischer, and Jean-Pierre Hubaux. Traci: an interface for coupling road traffic and network simulators. In Proceedings of the 11th communications and networking simulation symposium, pages 155–163, 2008.
[20] SC Yang, M Li, Y Lin, and TQ Tang. Electric vehicle’s electricity consumption on a road with different slope. Physica A: Statistical Mechanics and its Applications, 402:41–48, 2014.
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