© The Institution of Engineering and Technology
With the advancement of vehicle-to-vehicle and vehicle-to-infrastructure technologies, more and more real-time information regarding traffic and transportation system will be available to vehicles. This paper presents the development of a novel algorithm that uses available velocity bounds and powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt-2, the generated velocity trajectory saves fuel compared to a real-world drive cycle. The simulation results confirm the fuel consumption reduction with the rule-based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie.
References
-
-
1)
-
28. Johannesson, L., Asbogard, M., Egardt, B.: ‘Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming’, IEEE Trans. Intell. Transp. Syst., 2007, 8, (1), pp. 71–83.
-
2)
-
24. Silvas, E., Hereijgers, K., Peng, H., et al: ‘Synthesis of realistic driving cycles with high accuracy and computational speed, including slope information’, IEEE Trans. Veh. Technol., 2016, 65, (6), pp. 4118–4128.
-
3)
-
23. Chao, S., Xiaosong, H., Moura, S.J., et al: ‘Velocity predictors for predictive energy management in hybrid electric vehicles’, IEEE Trans. Control Syst. Technol., 2015, 23, (3), pp. 1197–1204.
-
4)
-
18. Gao, B.Z., Xiang, Y., Chen, H., et al: ‘Optimal trajectory planning of motor torque and clutch slip speed for gear shift of a two-speed electric vehicle’, J. Dyn. Syst. Meas. Control-Trans. ASME, 2015, 137, (6), p. 9.
-
5)
-
8. Moura, S.J., Fathy, H.K., Callaway, D.S., et al: ‘A stochastic optimal control approach for power management in plug-in hybrid electric vehicles’, IEEE Trans. Control Syst. Technol., 2011, 19, (3), pp. 545–555.
-
6)
-
6. Amoozadeh, M., Deng, H., Chuah, C.N., et al: ‘Platoon management with cooperative adaptive cruise control enabled by Vanet’, Veh. Commun., 2015, 2, (2), pp. 110–123.
-
7)
-
1. Groot, N., De Schutter, B., Hellendoorn, H.: ‘Toward system-optimal routing in traffic networks: a reverse Stackelberg game approach’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (1), pp. 29–40.
-
8)
-
27. Vögele, U., Endisch, C.: ‘Predictive vehicle velocity control using dynamic traffic information’, , 2016.
-
9)
-
9. Haitao, X.: ‘Eco-approach and departure techniques for connected vehicles at signalized traffic intersections’, , University of California Riverside, 2014.
-
10)
-
16. Wu, G.Y., Boriboonsomsin, K., Barth, M.J.: ‘Development and evaluation of an intelligent energy-management strategy for plug-in hybrid electric vehicles’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (3), pp. 1091–1100.
-
11)
-
10. Gunawan, F.E., Chandra, F.Y.: ‘Optimal averaging time for predicting traffic velocity using floating car data technique for advanced traveler information system’. The 9th Int. Conf. Traffic and Transportation Studies (ICTTS 2014), (Proceedia – Social and Behavioral Sciences, Shaoxing, China, 2014, pp. 566–575.
-
12)
-
17. Katsargyri, G.-E., Kolmanovsky, I.V., Michelini, J., et al: ‘Optimally controlling hybrid electric vehicles using path forecasting’. American Control Conf. IEEE, St. Louis, MO, USA, 2009, pp. 4613–4617.
-
13)
-
22. Moser, D., Waschl, H., Schmied, R., et al: ‘Short term prediction of a vehicle's velocity trajectory using its’, SAE Int. J. Passenger Cars-Electron. Electr. Syst., 2015, 8, (2), pp. 364–370.
-
14)
-
25. Ozatay, E., Onori, S., Wollaeger, J., et al: ‘Cloud-based velocity profile optimization for everyday driving: a dynamic-programming-based solution’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (6), pp. 2491–2505.
-
15)
-
7. Kamal, M.A.S., Mukai, M., Murata, J., et al: ‘Ecological driver assistance system using model-based anticipation of vehicle–road–traffic information’, IET Intell. Transp. Syst., 2010, 4, (4), p. 244.
-
16)
-
11. Rios-Torres, J., Malikopoulos, A.A.: ‘A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (5), pp. 1066–1077.
-
17)
-
21. Guo, L.L., Gao, B.Z., Gao, Y., et al: ‘Optimal energy management for HEVS in eco-driving applications using bi-level MPC’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (8), pp. 2153–2162.
-
18)
-
15. Servin, O., Boriboonsomsin, K., Barth, M.: ‘An energy and emissions impact evaluation of intelligent speed adaptation’. 2006 IEEE Intelligent Transportation Systems Conf., Toronto, Canada, 2006, pp. 1257–1262.
-
19)
-
20. Guo, L., Gao, B.Z., Chen, H.: ‘Online shift schedule optimization of 2-speed electric vehicle using moving horizon strategy’, IEEE-ASME Trans. Mechatron., 2016, 21, (6), pp. 2858–2869.
-
20)
-
13. Gonder, J., Earleywine, M., Sparks, W.: ‘Analyzing vehicle fuel saving opportunities through intelligent driver feedback’, SAE Int. J. Passenger Cars – Electron. Electr. Syst., 2012, 5, (2), pp. 450–461.
-
21)
-
14. Zhang, F.Q., Xi, J.Q., Langari, R.: ‘Real-time energy management strategy based on velocity forecasts using V2V and V2I communications’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (2), pp. 416–430.
-
22)
-
23)
-
4. Hu, J., Shao, Y.L., Sun, Z.X., et al: ‘Integrated vehicle and powertrain optimization for passenger vehicles with vehicle-infrastructure communication’, Transp. Res. Part C-Emerg. Technol., 2017, 79, pp. 85–102.
-
24)
-
2. Chen, W., Zhu, S., Li, D.: ‘Van: vehicle-assisted shortest-time path navigation’. The 7th IEEE Int. Conf. on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2010), San Francisco, CA, USA, 2010, pp. 442–451.
-
25)
-
3. Hamed, M.M., Almasaeid, H.R., Said, Z.M.B.: ‘Short-term prediction of traffic volume in urban arterials’, J. Transp. Eng., 1995, 121, (3), pp. 249–254.
-
26)
-
12. Engelbrecht, J., Booysen, M.J., Bruwer, F.J., et al: ‘Survey of smartphone-based sensing in vehicles for intelligent transportation system applications’, IET Intell. Transp. Syst., 2015, 9, (10), pp. 924–935.
-
27)
-
31. Boggs, P.T., Tolle, J.W.: ‘Sequential quadratic programming’, Acta Numer., 1995, 4, pp. 1–51.
-
28)
-
29)
-
19. Sciarretta, A., De Nunzio, G., Ojeda, L.L.: ‘Optimal ecodriving control energy-efficient driving of road vehicles as an optimal control problem’, IEEE Control Syst. Mag., 2015, 35, (5), pp. 71–90.
-
30)
-
30. Conlon, B.M., Blohm, T., Harpster, M., et al: ‘The next generation ‘Voltec’ extended range EV propulsion system’, SAE Int. J. Altern. Powertrains, 2015, 4, (2), pp. 248–259.
-
31)
-
5. Koenders, E., Vreeswijk, J.: ‘Cooperative infrastructure’. Intelligent Vehicles Symp., Eindhoven, Netherlands, 2008.
-
32)
-
26. Li, S.E., Li, K., Ahn, C., et al: ‘Mechanism of vehicular periodic operation for optimal fuel economy in free-driving scenarios’, IET Intell. Transp. Syst., 2015, 9, (3), pp. 306–313.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.5110
Related content
content/journals/10.1049/iet-its.2018.5110
pub_keyword,iet_inspecKeyword,pub_concept
6
6