Algorithmic analysis demonstrates high-accuracy adaptive cruise control in extended-range electric vehicles, suggesting improved safety and energy efficiency.
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.
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Zhang et al. (2024) studied this question.
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