The energy impact of adaptive cruise control in real-world highway multiple-car-following scenarios
Tóm tắt
Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.
Tài liệu tham khảo
Ahn, K. (1998). Microscopic fuel consumption and emission modeling (thesis) Virginia Tech.
Ahn, K., Rakha, H., Trani, A., & Van Aerde, M. (2002). Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. Journal of Transportation Engineering, 128, 182–190.
Bedner, E., Fulk, D., & Hac, A. (2007). Exploring the trade-off of handling stability and responsiveness with advanced control systems (SAE technical paper no. 2007–01–0812). Warrendale: SAE International. https://doi.org/10.4271/2007-01-0812.
Ciuffo, B., Makridis, M., Toledo, T., & Fontaras, G. (2018). Capability of current car-following models to reproduce vehicle free-flow acceleration dynamics. IEEE Transactions on Intelligent Transportation Systems, 19, 3594–3603.
Dvorkin, W., King, J., Gray, M., & Jao, S. (2019). Determining the greenhouse gas emissions benefit of an adaptive cruise control system using real-world driving data. Presented at the WCX SAE World Congress Experience. Detroit.
European Commission. (2016). A European strategy for low-emission mobility.
Faris, W. F., Rakha, H. A., Kafafy, R. I., Idres, M., & Elmoselhy, S. (2011). Vehicle fuel consumption and emission modelling: An in-depth literature review. International Journal of Vehicle Systems Modelling and Testing, 6, 318–395.
Fiori, C., Arcidiacono, V., Fontaras, G., Makridis, M., Mattas, K., Marzano, V., Thiel, C., & Ciuffo, B. (2019). The effect of electrified mobility on the relationship between traffic conditions and energy consumption. Transportation Research Part D: Transport and Environment, 67, 275–290.
Hellström, E., Åslund, J., & Nielsen, L. (2010). Design of an efficient algorithm for fuel-optimal look-ahead control. Control Engineering PracticeSpecial Issue on Automotive Control Applications, 2008 IFAC World Congress, 18, 1318–1327.
Jia, D., Lu, K., Wang, J., Zhang, X., & Shen, X. (2016). A survey on platoon-based vehicular cyber-physical systems. IEEE Communication Surveys and Tutorials, 18, 263–284.
Kamal, M. A. S., Mukai, M., Murata, J., & Kawabe, T. (2013). Model predictive control of vehicles on urban roads for improved fuel economy. IEEE Transactions on Control Systems Technology, 21, 831–841.
Kiam Heong, A., Chong, G., & Li, Y. (2005). PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13, 559–576. https://doi.org/10.1109/TCST.2005.847331.
Ko, Y., Song, B., & Oh, Y. (2019). Mathematical analysis of environmental effects of forming a platoon of smart vehicles. Sustainability, 11, 571.
Kohut, N. J., Karl Hedrick, P. J., & Borrelli, P. F. (2009). Integrating traffic data and model predictive control to improve fuel economy. IFAC Proceedings Volumes12th IFAC Symposium on Control in Transportation Systems, 42, 155–160.
Larue, G. S., Malik, H., Rakotonirainy, A., & Demmel, S. (2014). Fuel consumption and gas emissions of an automatic transmission vehicle following simple eco-driving instructions on urban roads. IET Intelligent Transport Systems, 8, 590–597.
Lei, C., van Eenennaam, E. M., Klein Wolterink, W., Ploeg, J., Karagiannis, G., & Heijenk, G. (2012). Evaluation of CACC string stability using SUMO, Simulink, and OMNeT++. EURASIP Journal on Wireless Communications and Networking, 2012, 116.
Li, L., Wang, X., & Song, J. (2017). Fuel consumption optimization for smart hybrid electric vehicle during a car-following process. Mechanical Systems and Signal ProcessingSignal Processing and Control challenges for Smart Vehicles, 87, 17–29.
Li, S., Li, K., Rajamani, R., & Wang, J. (2011). Model predictive multi-objective vehicular adaptive cruise control. IEEE Transactions on Control Systems Technology, 19, 556–566.
Li, S., Li, K., Wang, J., Zhang, L., Lian, X., Ukawa, H., & Bai, D. (2008). MPC based vehicular following control considering both fuel economy and tracking capability. In 2008 IEEE vehicle power and propulsion conference. Presented at the 2008 IEEE Vehicle Power and Propulsion Conference (pp. 1–6).
Luo, Y., Chen, T., Zhang, S., & Li, K. (2015). Intelligent hybrid electric vehicle ACC with coordinated control of tracking ability, fuel economy, and ride comfort. IEEE Transactions on Intelligent Transportation Systems, 16, 2303–2308.
Ma, G., Ghasemi, M., & Song, X. (2018). Integrated powertrain energy management and vehicle coordination for multiple connected hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 67, 2893–2899.
Makridis, M., Fontaras, G., Ciuffo, B., & Mattas, K. (2019). MFC free-flow model: Introducing vehicle dynamics in microsimulation. Transportation Research Record. https://doi.org/10.1177/0361198119838515.
Makridis, M., Mattas, K., Borio, D., Giuliani, R., & Ciuffo, B. (2018). Estimating reaction time in adaptive cruise control system. In 2018 IEEE intelligent vehicles symposium (IV) (pp. 1312–1317).
Makridis, M., Mattas, K., & Ciuffo, B. (2019). Response time and time headway of an adaptive cruise control. An empirical characterization and potential impacts on road capacity. IEEE Transactions on Intelligent Transportation Systems, 1–10. Early Access.
Mamouei, M., Kaparias, I., & Halikias, G. (2018). A framework for user- and system-oriented optimisation of fuel efficiency and traffic flow in adaptive cruise control. Transportation Research Part C: Emerging Technologies, 92, 27–41.
Markschläger, P., Wahl, H.-G., Weberbauer, F., & Lederer, M. (2012). Assistance system for higher fuel efficiency. Auto Tech Review, 1, 40–45.
MathWorks. (2019). 2D correlation coefficient https://uk.mathworks.com/help/images/ref/corr2.html.
Munyaneza, O., Munyazikwiye, B. B., & Karimi, H. R. (2015). Speed control design for a vehicle system using fuzzy logic and PID controller. In Presented at the 2015 international conference on fuzzy theory and its applications (iFUZZY) (pp. 56–61).
Osman, K., Rahmat, M. F., & Ahmad, M. A. (2009). Modelling and controller design for a cruise control system (pp. 254–258). Presented at the 2009 5th International Colloquium on Signal Processing Its Applications. Penang.
Park, S., Rakha, H., Ahn, K., & Moran, K. (2013). Fuel economy impacts of manual, conventional cruise control, and predictive eco-cruise control driving. International Journal of Transportation Science and Technology, 2, 227–242.
Raposo, A. (2017). The r-evolution of driving: From connected vehicles to coordinated automated road transport (C-ART). European Commission. Publications office of the European Union.
Themann, P., Bock, J., & Eckstein, L. (2015). Optimisation of energy efficiency based on average driving behaviour and driver’s preferences for automated driving. IET Intelligent Transport Systems, 9, 50–58.
Vajedi, M., & Azad, N. L. (2016). Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control. IEEE Transactions on Intelligent Transportation Systems, 17, 113–122.
Wu, C., Zhao, G., & Ou, B. (2011). A fuel economy optimization system with applications in vehicles with human drivers and autonomous vehicles. Transportation Research Part D: Transport and Environment, 16, 515–524.
Xie, S., Hu, X., Liu, T., Qi, S., Lang, K., & Li, H. (2019). Predictive vehicle-following power management for plug-in hybrid electric vehicles. Energy, 166, 701–714.
Zhao, R. C., Wong, P. K., Xie, Z. C., & Zhao, J. (2017). Real-time weighted multi-objective model predictive controller for adaptive cruise control systems. International Journal of Automotive Technology, 18, 279–292.
Zhao, W., Ngoduy, D., Shepherd, S., Liu, R., & Papageorgiou, M. (2018). A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection. Transportation Research Part C: Emerging Technologies, 95, 802–821.
Zhou, M., Jin, H., & Wang, W. (2016). A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing. Transportation Research Part D: Transport and Environment, 49, 203–218.