Personalized Control Strategy of Electronic Brake Booster With Driving Behaviors Identification

Abstract

To improve the comfort and user acceptance of the intelligent vehicle braking system, this paper proposes a control strategy for the electronic brake booster (Ebooster) that considers differences in driving behaviors. First, a real-vehicle driving data collection platform is built to collect the driving behavior data of 84 drivers under typical vehicle-following braking conditions. Subsequently, the k-means clustering algorithm optimized by particle swarm optimization (PSO) is used to realize the cluster analysis of driving behaviors, and the support vector machine (SVM) algorithm optimized by the genetic algorithm (GA) is used to complete the identification of driving behaviors. Next, through the analysis of the boost characteristics of the self-designed Ebooster, driving behaviors are matched with boost characteristics, and a personalized basic boosting control strategy based on the driver behavior is designed. Finally, a hardware-in-the-loop (HiL) bench and a real-vehicle test platform are designed and built to verify the effectiveness of the personalized control strategy of the Ebooster. The results show that this method can effectively identify the type of driver and make personalized adjustments to the basic boosting strategy of the Ebooster, which effectively improves the personalization of the braking system, resulting in a higher pedal response and user acceptance.