对分布式驱动电动汽车的模型预测直接运动控制的参数优化,考虑到效率和驾驶感觉
1School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了分布式驱动电动汽车 (DDEV) 的新型预测式直接运动控制 (MPDMC) 模型,优化纵向和横向运动以提高效率和驾驶感. 粒子群集优化 (PSO) 和LSTM网络在各种驾驶条件下提高了控制性能.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 分布式驱动电动汽车 (DDEV) 需要先进的控制策略,以同时进行纵向和横向运动管理.
- 在电动汽车 (EV) 控制系统中,整合车辆动态,电机控制和驾驶员舒适度是一个重大挑战.
研究的目的:
- 为DDEVs开发和验证一种新型模型预测直接运动控制 (MPDMC) 策略.
- 通过同时优化纵向/侧向运动,能源效率和驾驶体验来提高车辆性能.
- 研究粒子群优化 (PSO) 和长短期记忆 (LSTM) 神经网络在改进控制参数方面的有效性.
主要方法:
- 对DDEV动态模型的分析,包括车身和车轮内电机的特性.
- 开发一种使用单个CPU生成基于最小化成本函数的电压引用的MPDMC方法.
- 构建一个考虑纵向速度,曲率,横向位移,效率和驾驶感觉的成本函数,通过PSO和基于LSTM的驾驶感觉评估进行优化.
主要成果:
- 在模拟中,优化的MPDMC策略与基线方法相比,表现优越.
- 综合方法有效地平衡了车辆动态,能源效率和主观驾驶质量,跨越了"正常"",生态"和"运动"模式.
- MATLAB 和 CarSim 模拟验证了 MPDMC 策略在四个不同的驾驶场景中的有效性.
结论:
- 拟议的MPDMC战略为控制DDEV提供了强大而高效的解决方案.
- MPC,PSO和LSTM的组合为优化复杂的车辆控制目标提供了一个强大的框架.
- 这项研究有助于智能汽车控制系统的进步,提高性能和驾驶员满意度.
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