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Under the background of national ‘carbon compensation and carbon neutrality’, the research on the economic driving strategy of automobiles integrated with connected environment is crucial to the development of energy conservation and emission reduction in automobile industry. Aiming at the problem that the existing energy management strategies (EMSs) of new energy vehicles are developed based on fixed conditions without considering the actual road conditions. In this work, the four-wheel drive pure electric vehicle in connected environment is taken as the research object, and the economic driving strategy based on real-time traffic information in urban conditions is studied. A hierarchical energy management method for intelligent connected vehicles composed of the upper model predictive control controller and the lower dynamic coordination controller is proposed. In the upper control layer, the economic speed sequence is predicted by using the real-time traffic information of the planned path and the model predictive control algorithm. In the lower control layer, the dynamic coordinated control of the optimal power and optimal torque of the vehicle system is carried out by combining the vehicle model and the thermal management system. The cooperative control optimization effect of multi-objective, multi-constraint and multi-system coupling of vehicle driving process over a wide temperature range is realized. The simulation results demonstrate that compared with the traditional energy management strategy, the proposed method has significant performance in terms of condition adaptability and computational efficiency. Under urban conditions, it can effectively avoid idle parking at red lights, significantly reduce the number of acceleration and deceleration behaviors, and achieve the effect of ‘green wave passage’. Moreover, the driving energy consumption can be reduced by more than 25%, and the working state between the front motor and the rear motor is coordinated and optimized to achieve the energy balance control effect of the power system. These results indicate that the proposed method effectively improves the driving comfort and the driving economy of vehicles.

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Research on Speed Optimization Control and Energy Management Strategy of Pure Electric Vehicles Based on Real-Time Traffic Information

  • 会议论文

  • 发表日期：

    2026年

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  • Zhao Binggen,
  • Li Song,
  • Zhang Dancheng,
  • Chen Song,
  • Zhang Zhihu,
  • Wang Guanxiong

摘要

Under the background of national ‘carbon compensation and carbon neutrality’, the research on the economic driving strategy of automobiles integrated with connected environment is crucial to the development of energy conservation and emission reduction in automobile industry. Aiming at the problem that the existing energy management strategies (EMSs) of new energy vehicles are developed based on fixed conditions without considering the actual road conditions. In this work, the four-wheel drive pure electric vehicle in connected environment is taken as the research object, and the economic driving strategy based on real-time traffic information in urban conditions is studied. A hierarchical energy management method for intelligent connected vehicles composed of the upper model predictive control controller and the lower dynamic coordination controller is proposed. In the upper control layer, the economic speed sequence is predicted by using the real-time traffic information of the planned path and the model predictive control algorithm. In the lower control layer, the dynamic coordinated control of the optimal power and optimal torque of the vehicle system is carried out by combining the vehicle model and the thermal management system. The cooperative control optimization effect of multi-objective, multi-constraint and multi-system coupling of vehicle driving process over a wide temperature range is realized. The simulation results demonstrate that compared with the traditional energy management strategy, the proposed method has significant performance in terms of condition adaptability and computational efficiency. Under urban conditions, it can effectively avoid idle parking at red lights, significantly reduce the number of acceleration and deceleration behaviors, and achieve the effect of ‘green wave passage’. Moreover, the driving energy consumption can be reduced by more than 25%, and the working state between the front motor and the rear motor is coordinated and optimized to achieve the energy balance control effect of the power system. These results indicate that the proposed method effectively improves the driving comfort and the driving economy of vehicles.