Research on personalized control strategy of EHB system for consistent braking feeling considering driving behaviors

Ruijun Zhang, Wanzhong Zhao, Chunyan Wang and Kang Tai

Energy, 2024, vol. 293, issue C

Abstract: Since the existing control strategy for electro-hydraulic composite braking (EHB) system is essentially "vehicle-centered", it is liable to cause incompatibility with current driving behavior, torque fluctuation and inconsistent braking feeling occur, which affects braking safety. In view of the above issue, a personalized MPC control strategy in accordance with the design methodology of characteristic E is presented. To do this, the data collection platform for characterizing driving behavior is constructed under typical vehicle-following conditions. Then, a generalized radial basis function (GRBF) neural network is adopted to accurately identify braking intensity of different driving behaviors. Next, an optimization model for the maximum energy recovery of EHB system is established in terms of required braking torque and motor speed, the distribution coefficient of braking torque is optimized by applying the adaptive particle swarm optimization (APSO) algorithm. Finally, the proposed personalized MPC control strategy is verified under different driving behaviors, the results display that: (1) the personalized MPC controller possess superiority of acquiring stable braking feeling, the torque tracking error is decreased by 96.8 %; (2) energy recovery for EHB system with optimized torque distribution is increased by 34.47 % under FTP-75 cycle conditions, and the response amplitude of braking feeling is increased by 5.6 %.

Keywords: EHB system; Braking feeling; APSO algorithm; Torque distribution; GRBF neural network; Personalized MPC controller (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003402

DOI: 10.1016/j.energy.2024.130568

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