Skip to main content
Log in

Fuel consumption reduction effect of pre-acceleration before gliding of a vehicle with free-wheeling

  • Research Article
  • Published:
Control Theory and Technology Aims and scope Submit manuscript

Abstract

Advanced fuel economy strategies are expected to reduce the fuel consumption of vehicles. An internal combustion engine (ICE) driving vehicle equipped with free-wheeling turns off the fuel injection and decouples the engine from the drivetrain when the driving force is not required. This paper proposes a method to reduce the fuel consumption of a vehicle equipped with free-wheeling. First, an optimization problem is formulated to minimize the fuel consumption of a vehicle with free-wheeling when the traveling distance, the initial and final speed are specified and the vehicle needs to glide before arriving at the end point for fuel economy. The speed profile of the vehicle, engine operating point, and engine on/off timing are obtained as the results of the optimization. The analytical and numerical analyses results demonstrate the effectiveness and the fuel-saving mechanism of the obtained speed profile. The main finding of the analyses is that rather than starting a gliding stage immediately after an acceleration or a constant speed stage, adding a pre-acceleration stage before the gliding stage is more fuel-economic under some conditions independent of the complexity of the vehicle model. The obtained speed profile including a pre-acceleration stage is applied to a driving scenario including traffic congestions. The results demonstrate the effectiveness of the pre-acceleration stage in reducing fuel consumption for a vehicle equipped with free-wheeling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. U.S. Energy Information Administration. Use of energy explained: Energy use for transportation. https://www.eia.gov/energyexplained/use-of-energy/transportation.php.

  2. Jollands, N., Waide, P., Ellis, M., & Onoda, T. (2010). The 25 IEA energy efficiency policy recommendations to the G8 gleneagles plan of action. Energy Policy, 38(11), 6409–6418.

    Article  Google Scholar 

  3. Kamal, M. A. S., Mukai, M., Murata, J., & Kawabe, T. (2010). On board ECO-driving system for varying road-traffic environments using model predictive control. In IEEE international conference on control applications, Yokohama (pp. 1636–1641).

  4. So, K. M., Gruber, P., Tavernini, D., Karci, A. E. H., Sorniotti, A., & Motaln, T. (2020). On the optimal speed profile for electric vehicles. IEEE Access, 8, 78504–78518.

    Article  Google Scholar 

  5. Henriksson, M., Flärdh, O., & Mårtensson, J. (2017). Optimal powertrain control of a heavy-duty vehicle under varying speed requirements. In IEEE 20th international conference on intelligent transportation systems (ITSC)) (pp. 1562–1573).

  6. Xu, B., Ban, X. J., Bian, Y., Li, W., Wang, J., Li, S. E., & Li, K. (2019). Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections. IEEE Transactions on Intelligent Transportation Systems, 20(4), 1390–1403.

    Article  Google Scholar 

  7. Pariota, L., Costanzo, L. D., Coppola, A., Aniello C. D’, & Bifulco, G. N. (2019). Green light optimal speed advisory: a C-ITS to improve mobility and pollution. In IEEE international conference on environment and electrical engineering and IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe), Genova (pp. 1–6). https://doi.org/10.1109/EEEIC.2019.8783573.

  8. Chen, H., Zuo, C., & Yuan, Y. (2013). Control strategy research of engine smart start/stop system for a micro car. In SAE 2013 world congress & exhibition. https://doi.org/10.4271/2013-01-0585.

  9. Bishop, J., Nedungadi, A., Ostrowski, G., Surampudi, B., Armiroli, P., & Taspinar, E. (2007). An engine start/stop system for improved fuel economy. In SAE world congress & exhibition. https://doi.org/10.4271/2007-01-1777.

  10. Mueller, N., Strauss, S., Tumback, S., Goh, G. .-C., & Christ, A. (2011). Next generation engine start/stop systems: “Free-wheeling’’. SAE International Journal of Engines, 4(1), 874–887.

    Article  Google Scholar 

  11. Griefnow, P., & Andert, J. (2016). Impact of sailing strategies on fuel consumption and the powernet system. In 2nd international conference and exhibition on automobile engineering, Valencia.

  12. Tashiro, N., Imanishi, Y., Inaba, R., & Okada, T. (2017). Engine and transmission control using the prediction information for sailing stop system. Transactions of Society of Automotive Engineers of Japan, 48(2), 581–586.

    Google Scholar 

  13. Stroe, N., Olaru, S., Colin, G., Ben-Cherif, K., & Chamaillard, Y. (2019). Predictive control framework for HEV: Energy management and free-wheeling analysis. IEEE Transactions on Intelligent Vehicles, 4(2), 220–231.

  14. Beusen, B., Broekx, S., Denys, T., Beckx, C., Degraeuwe, B., Gijsbers, M., Scheepers, K., Govaerts, L., Torfs, R., & Panis, L. I. (2009). Using on-board logging devices to study the long-term impact of an eco-driving course. Transportation Research Part D: Transport and Environment, 14(7), 514–520.

    Article  Google Scholar 

  15. Zarkadoula, M., Zoidis, G., & Tritopoulou, E. (2007). Training urban bus drivers to promote smart driving: A note on a Greek eco-driving pilot program. Transportation Research Part D: Transport and Environment, 12(6), 449–451.

    Article  Google Scholar 

  16. Fu, Q., Xu, F., Shen, T., & Takai, K. (2020). Distributed optimal energy consumption control of HEVs under MFG based speed consensus. Control Theory and Technology, 18(2), 115–125.

    Article  MathSciNet  MATH  Google Scholar 

  17. Zhang, J., & Xu, F. (2020). Real-time optimization of energy consumption under adaptive cruise control for connected HEVs. Control Theory and Technology, 18(2), 182–192.

    Article  MathSciNet  MATH  Google Scholar 

  18. Zhang, B., Cao, W., & Shen, T. (2019). Two-stage on-board optimization of merging velocity planning with energy management for HEVs. Control Theory and Technology, 17(4), 335–345.

    Article  MathSciNet  Google Scholar 

  19. Lu, Y., Xu, X., Ding, C., & Lu, G. (2019). A speed control method at successive signalized intersections under connected vehicles environment. IEEE Intelligent Transportation Systems Magazine, 11(3), 117–128.

    Article  Google Scholar 

  20. Mahler, G., & Vahidi, A. (2014). An optimal velocity-planning scheme for vehicle energy efficiency through probabilistic prediction of traffic-signal timing. IEEE Transactions on Intelligent Transportation Systems, 15(6), 2516–2523.

    Article  Google Scholar 

  21. Yang, H., Rakha, H., & Ala, M. V. (2017). Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Transactions on Intelligent Transportation Systems, 18(6), 1575–1585.

    Google Scholar 

  22. Zhang, B., Gao, Z., & Guo, G. (2017). Fuel optimal vehicle control via traffic light prediction. In 36th Chinese control conference, Dalian (pp. 10004–10009).

  23. Yamaguchi, D., Kamal, M. A. S., Mukai, M., & Kawabe, T. (2016). Model predictive control for automobile ecological driving using traffic signal information. Journal of System Design and Dynamics, 6(3), 297–309.

    Article  Google Scholar 

  24. Asadi, B., & Vahidi, A. (2011). Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Transactions on Control Systems Technology, 19(3), 707–714.

    Article  Google Scholar 

  25. Lin, Q., Li, S. E., Du, X., Zhang, X., Peng, H., Luo, Y., & Li, K. (2018). Minimize the fuel consumption of connected vehicles between two red-signalized intersections in urban traffic. IEEE Transactions on Vehicular Technology, 67(10), 9060–9072.

    Article  Google Scholar 

  26. Li, S. E., & Peng, H. (2012). Strategies to minimize fuel consumption of passenger cars during car-following scenarios. Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, 226(3), 419–429.

    Google Scholar 

  27. Zhang, F., Hu, X., Langari, R., Wang, L., Cui, Y., & Pang, H. (2021). Adaptive energy management in automated hybrid electric vehicles with flexible torque request. Energy, 214, 118873.

    Article  Google Scholar 

  28. He, H., Wang, Y., Li, J., Dou, J., Lian, R., & Li, Y. (2021). An improved energy management strategy for hybrid electric vehicles integrating multistates of vehicle-traffic information. IEEE Transactions on Transportation Electrification, 7(3), 1161–1172.

    Article  Google Scholar 

  29. Geng, W., Lou, D., Wang, C., & Zhang, T. (2020). A cascaded energy management optimization method of multimode power-split hybrid electric vehicles. Energy, 199, 117224.

    Article  Google Scholar 

  30. Narita, S., Shi, H., & Shen, T. (2020). Optimal energy management strategy for HEVs with consideration of engine on-off transient operation. In SICE international symposium on control systems (CD-ROM) (pp. 1–4).

  31. Tang, L., Rizzoni, G., & Onori, S. (2015). Energy management strategy for HEVs including battery life optimization. IEEE Transactions on Transportation Electrification, 1(3), 211–222.

    Article  Google Scholar 

  32. Jiang, Q., Ossart, F., & Marchand, C. (2017). Comparative study of real-time HEV energy management strategies. IEEE Transactions on Vehicular Technology, 66(12), 10875–10888.

    Article  Google Scholar 

  33. Shieh, S.-Y., Ersal, T., & Peng, H. (2019). Pulse-and-glide operation for parallel hybrid electric vehicles with step-gear transmission in automated car-following scenario with ride comfort consideration. In American control conference, Philadelphia (pp. 959–964).

  34. Turri, V., Besselink, B., & Johansson, K. H. (2016). Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning. IEEE Transactions on Control Systems Technology, 25(1), 12–28.

    Article  Google Scholar 

  35. Li, S. E., Li, R., Wang, J., Hu, X., Cheng, B., & Li, K. (2016). Stabilizing periodic control of automated vehicle platoon with minimized fuel consumption. IEEE Transactions on Transportation Electrification, 3(1), 259–271.

    Article  Google Scholar 

  36. Li, S. E., Xu, S., Li, G., & Cheng, B. (2014). Periodicity based cruising control of passenger cars for optimized fuel consumption. IEEE Intelligent Vehicles Symposium Proceedings, 2014, 1097–1102.

    Google Scholar 

  37. Sohn, C., Andert, J., & Jolovic, D. (2020). An analysis of the tradeoff between fuel consumption and ride comfort for the pulse and glide driving strategy. IEEE Transactions on Vehicular Technology, 69(7), 7223–7233.

    Article  Google Scholar 

  38. Xu, S., Li, S. E., Zhang, X., Cheng, B., & Peng, H. (2015). Fuel-optimal cruising strategy for road vehicles with step-gear mechanical transmission. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3496–3507.

    Article  Google Scholar 

  39. Xu, S., Li, S. E., Peng, H., Cheng, B., Zhang, X., & Pan, Z. (2016). Fuel-saving cruising strategies for parallel HEVs. IEEE Transactions on Vehicular Technology, 65(6), 4676–4686.

    Article  Google Scholar 

  40. TUM. FALCON.m. https://www.fsd.mw.tum.de/software/falcon-m/.

  41. Nocedal, J., Wächter, A., & Waltz, R. A. (2009). Adaptive barrier update strategies for nonlinear interior methods. SIAM Journal on Optimization, 19(4), 1674–1693.

    Article  MathSciNet  MATH  Google Scholar 

  42. Wächter, A. (2002). An interior point algorithm for large-scale nonlinear optimization with applications in process engineering. Ph.D. thesis. Pittsburgh, PA: Carnegie Mellon University.

  43. Wächter, A., & Biegler, L. T. (2002). On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming, 106(1), 25–57.

    Article  MathSciNet  MATH  Google Scholar 

  44. Praptijanto, A., Santoso, W. B., Nur, A., Wahono, B., & Putrasari, Y. (2017). Performance and driveline analyses of engine capacity in range extender engine hybrid vehicle. In International conference on engineering, science and nanotechnology, Solo. https://doi.org/10.1063/1.4968254.

  45. Xu, J., Yang, K., Shao, Y., & Lu, G. (2015). An experimental study on lateral acceleration of cars in different environments in Sichuan, Southwest China. Discrete Dynamics in Nature and Society, 2015, e494130. https://doi.org/10.1155/2015/494130.

    Article  Google Scholar 

  46. Liu, R., Zhao, X., Zhu, X., & Ma, J. (2021). Statistical characteristics of driver acceleration behavior and its probability model. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.https://doi.org/10.1177/09544070211018039.

    Article  Google Scholar 

  47. Li, S. E., Xu, S., Huang, X., Cheng, B., & Peng, H. (2015). Eco-departure of connected vehicles with V2X communication at signalized intersections. IEEE Transactions on Vehicular Technology, 64(12), 5439–5449.

    Article  Google Scholar 

  48. Kamal, M., Mukai, M., Murata, J., & Kawabe, T. (2011). Ecological driving based on preceding vehicle prediction using MPC. IFAC Proceedings Volumes, 44(1), 3843–3848.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wenjing Cao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, W., Kawabe, T., Yuno, T. et al. Fuel consumption reduction effect of pre-acceleration before gliding of a vehicle with free-wheeling. Control Theory Technol. 20, 235–247 (2022). https://doi.org/10.1007/s11768-022-00087-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11768-022-00087-x

Keywords

Navigation