An efficiency control strategy of dual-motor multi-gear drive algorithm

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lijun Xiao, Wei Liang, Jiahong Cai, Ming Wang, Jiahong Xiao, Yinyan Gong, Weigang Zhang
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引用次数: 0

Abstract

The Dual-motor multi-gear coupling powertrain (DMCP) has the potential to improve transmission system efficiency and driving comfort, but its complex structure and multiple working modes present challenges. The switching between different modes is easy to cause longitudinal biggish vehicle jerk. To address these issues,this paper introduces the Deep Deterministic Policy Gradient (DDPG) algorithm in the design of an Energy Management Strategy (EMS) that minimises total drive power consumption. And the number of working modes is divided and simplified. The process of switching dual motor and single motor to single motor is introduced in detail. The simulation results using AMESim and MATLAB show that the energy management strategy can effectively improve the economy, achieve no power interruption during mode switching, shift impact is less than 8m/s3, and output torque is remains stable.
双电机多齿轮驱动算法的高效控制策略
双电机多齿轮耦合动力系统(DMCP)具有提高传动系统效率和驾驶舒适性的潜力,但其复杂的结构和多种工作模式给汽车带来了挑战。不同模式之间的切换容易造成车辆纵向较大的抖动。为了解决这些问题,本文在能量管理策略(EMS)的设计中引入了深度确定性策略梯度(DDPG)算法,以最小化总驱动功耗。并对工作模式的数量进行了划分和简化。详细介绍了双电机和单电机切换到单电机的过程。基于AMESim和MATLAB的仿真结果表明,能量管理策略能有效提高经济性,实现模式切换时无电源中断,换档冲击小于8m/s3,输出转矩保持稳定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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