Adaptive brake energy recovery strategy considering traffic information

In order to improve the adaptability of the braking energy recovery process of intelligent vehicle, a traffic information-based adaptive braking strategy is proposed in this paper. In the multi-objective optimization framework, driving safety constraints, traffic law limitations, comfort requirements, and energy recovery efficiency are integrated into consideration. The terminal demands of the braking objectives are transformed into a constraint problem for a model predictive controller with a decreasing sampling distance. Traffic information, along with the demands of comfort and safety, are transformed into real-time torque thresholds through the model and state information of vehicle. The real-time braking torque range is updated through the set of thresholds, which is dynamically adjusted by a combination of real-time road condition information and braking objectives to ensure the adaptability of various driving conditions. The optimal motor and hydraulic torque are obtained by optimizing within the real-time and dynamic range of torque constraints. Several scenarios are given under co-simulation of CarSim and Matlab/Simulink. The results show that the proposed adaptive method is able to ensure safety and meet traffic regulations in different driving conditions compared to the energy-efficient speed optimization strategy, and the SOC increased by 6% compared to the rule-based strategy, verifying the effectiveness of the proposed method.