A Particle Swarm Optimization and Ant Colony Optimization Fusion Algorithm-based Model Predictve Torque Coordnation Control Strategy for Distributed Electric Drive Vehicle

被引：0
|
作者：
Zhang Y. [1 ,2 ]
Xiang C. [1 ]
Wang W. [1 ]
Chen Y. [3 ]
机构：
[1] School of Mechanical Engineering, Beijing institute of Technology, Beijing
[2] Beijing Electromechanical Engineering Research Institute, Beijing
[3] China North Vehicle Research Institute, Beijing
来源：
Binggong Xuebao/Acta Armamentarii | 2023年 / 44卷 / 11期
关键词：
ant colony; distributed electric drive vehicle; model predictive control; particle swarm optimization algorithm; torque coordination control;
D O I：
10.12382/bgxb.2022.0819
中图分类号：
学科分类号：
摘要：
For the dynamic control challenges caused by the coupling effect of multiple power sources and high nonlinearity in distributed electric drive vehicle, a model predictive torque coordination control strategy based on particle swarm optimization and ant colony optimization is proposed, which uses a 7-degree-of-freedom vehicle dynamics model as the prediction model. The simulation and actual vehicle test platforms were built, and the multiple operating conditions were test. The test results show that the proposed torque coordination control strategy can be used to adjust the control mode according to the experimental conditions, thus achieving a comprehensive optimal control effect of power, economy, and handling stability. © 2023 China Ordnance Society. All rights reserved.
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收藏
页码：3253 / 3268
页数：15
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