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Energy-optimal adaptive cruise control strategy for electric vehicles based on model predictive control

Author

Listed:
  • Pan, Chaofeng
  • Huang, Aibao
  • Wang, Jian
  • Chen, Liao
  • Liang, Jun
  • Zhou, Weiqi
  • Wang, Limei
  • Yang, Jufeng

Abstract

Adaptive cruise control (ACC), as an important part of the process of realizing self driving, is very consistent with the current concept of vehicle production vigorously advocating safety, energy saving and environmental protection. However, how to integrate energy optimization into the design of the controller and realize the trade-off between the tracking performance and economy for the host vehicle is a very important problem. Therefore, this paper proposes a strategy for energy-optimal adaptive cruise control (EACC) for pure electric vehicles based on model predictive control (MPC) algorithm. By the way to introduce motor energy consumption as the economic evaluation index, the optimized torque can make the host vehicle track the preceding vehicle more economically with the constraints related to safety, comfort and tracking satisfied. At the same time, based on the nonlinear autoregressive with external input (NARX), the acceleration prediction of the preceding vehicle is selected as the disturbance for MPC, which can effectively improve the speed tracking performance for the host vehicle in the situation of driving at low speed and braking at high speed. In addition, the variable weight strategy is used to balance tracking performance and economy, which improves the adaptability of EACC vehicles under different working conditions like high speed and low speed. The results under the cycle condition of NEDC and WVUCITY show that the proposed EACC based on MPC+ (introducing leading vehicle speed prediction and motor energy consumption) shows a good energy saving compared with the other strategies based on PID, LQR and MPC. Compared with the classic Eco-ACC strategy based on dynamic programming, the proposed MPC + control strategy has the advantage of realizing more gentle torque output, thus the peak of the battery discharge current is relatively less than that of the controller based on DP, which is beneficial for prolong the battery life. The hardware-in-loop test evaluates the response characteristics of the controller and validates the effectiveness of the proposed MPC + strategy under real-time conditions.

Suggested Citation

  • Pan, Chaofeng & Huang, Aibao & Wang, Jian & Chen, Liao & Liang, Jun & Zhou, Weiqi & Wang, Limei & Yang, Jufeng, 2022. "Energy-optimal adaptive cruise control strategy for electric vehicles based on model predictive control," Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221030425
    DOI: 10.1016/j.energy.2021.122793
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    References listed on IDEAS

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    1. Xie, Shaobo & Lang, Kun & Qi, Shanwei, 2020. "Aerodynamic-aware coordinated control of following speed and power distribution for hybrid electric trucks," Energy, Elsevier, vol. 209(C).
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    Cited by:

    1. Jia, Xingyun & Zhou, Dengji, 2024. "Multi-variable anti-disturbance controller with state-dependent switching law for adaptive cycle engine," Energy, Elsevier, vol. 288(C).

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