Design of adaptive cruise control strategy for EREV considering driving behavior

Abstract

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IntroductionTraditional adaptive cruise control systems ignore the impact of the driver’s intentions and driving behavior on system performance.MethodsIn response to this issue, this study designs a new adaptive cruise control system by combining personalized driving style recognition, dynamic distance control, prospective energy management, and a model predictive control framework that integrates long short-term memory neural networks and ensemble learning.ResultsIt was verified that the accuracy of the algorithm was 96.2%. In addition, experts had average ratings of 95, 96, and 98 for the economy, safety, and comfort of the system, respectively.DiscussionThis model is expected to achieve comprehensive performance optimization and improvement of EREV in complex driving environments, injecting new vitality and power into the intelligent development of electric vehicles.

Keywords

• LSTM
• EREV
• driving behavior model
• cruise control
• MPC