Adaptive brake energy recovery strategy considering traffic information

Haiyan Zhao, Hongbin Xie, Yan Zhao, Xinghao Lu, Bingzhao Gao and Hong Chen

Energy, 2025, vol. 321, issue C

Abstract: 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.

Keywords: Adaptive control; Traffic information; Torque distribution; Energy recovery (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:321:y:2025:i:c:s0360544225009880

DOI: 10.1016/j.energy.2025.135346

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