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ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Engine and Automotive Engineering
Volume 10 - 2024 |
doi: 10.3389/fmech.2024.1408277
Design of Adaptive Cruise Control Strategy for EREV Considering Driving Behavior
Provisionally accepted- Foshan Polytechnic, Foshan, Guangdong Province, China
Traditional adaptive cruise control systems ignore the impact of the driver's intentions and driving behavior on system performance. 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. 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. 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.
Keywords: LSTM, EREV, Driving behavior model, cruise control, MPC
Received: 28 Mar 2024; Accepted: 23 Jul 2024.
Copyright: © 2024 Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Tao Wang, Foshan Polytechnic, Foshan, Guangdong Province, China
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