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Energy efficiency optimization of passenger vehicles considering aerodynamic wake flow influence in car-following scenarios

Author

Listed:
  • Lei, Zhenzhen
  • Wan, Wenjun
  • Xue, Along
  • Zeng, Chao
  • Zhang, Yuanjian
  • Chen, Zheng
  • Liu, Yonggang

Abstract

Air resistance plays a crucial role in enhancing fuel efficiency and reducing emissions in vehicle platoons. This paper examines the impact of passenger vehicle’s wake flow on drag reduction in various following scenarios and proposes an efficient following strategy to optimize fuel economy while ensuring vehicle safety. First, the outflow field of passenger vehicle queue is theoretically analyzed, and an aerodynamic simulation model is established. Next, an equivalent drag coefficient estimation method is introduced by incorporating vehicle speed and following distance. Furthermore, a following through control strategy for drag reduction is proposed based on the estimation model. To optimize both safety and drag reduction in real driving conditions, a soft constraint on the equivalent drag coefficient is applied using flexible boundary conditions. Simulation results demonstrate that the proposed following through control strategy, with adaptive drag reduction, can reduce the equivalent drag coefficient by up to 10.77 % and improve average energy efficiency by 9.76 %, highlighting the significant potential of following drag reduction in autonomous driving.

Suggested Citation

  • Lei, Zhenzhen & Wan, Wenjun & Xue, Along & Zeng, Chao & Zhang, Yuanjian & Chen, Zheng & Liu, Yonggang, 2025. "Energy efficiency optimization of passenger vehicles considering aerodynamic wake flow influence in car-following scenarios," Energy, Elsevier, vol. 328(C).
  • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s0360544225021437
    DOI: 10.1016/j.energy.2025.136501
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    References listed on IDEAS

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    1. Yu, Shuyou & Sun, Shaoyu & Chen, Hong & Liu, Yangfan & Li, Wenbo & Kim, Jung-Su, 2025. "Learning based predictive control of truck platoons with speed planning," Energy, Elsevier, vol. 337(C).

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