Design of adaptive cruise control strategy for EREV considering driving behavior.

Introduction: Traditional adaptive cruise control systems ignore the impact of the driver's intentions and driving behavior on system performance. Methods: In 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. Results: It 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. Discussion: This 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. [ABSTRACT FROM AUTHOR]