Abstract

The use of electric mobility must be part of transportation in the future. The detection, assessment, and scenario of defects in electric drives improve the trustworthiness of electric cars (EV). Permanent magnet synchronous motor (PMSM) drives are worn in a multiplicity of usage appropriate to their enhanced tactical suppleness, superior control thickness, and higher efficiency. In this learning, quick digital twins (i-DT) fashioned in MATLAB/Simulink are used to build PMSM monitoring system and prognosis. An artificial neural system (ANN) in addition to fuzzy logic be used to map the source expanse, point in point in time of EV take a trip, and outputs exterior temp, twisting hotness, moment to fill up the comportment lubricant, and division weakening of magnetic field in charge to determine the lingering constructive life (LCL) of a permanent magnet (PM).This is carried out within the context of linked vehicles and serves as an illustration of the possible advantages that cloud computing, traffic data, and intelligent transportation systems (ITS) may provide for enhancing PHEV energy management. A trend analysis of future advancements in optimization algorithm progress, development criteria, PHEV mixing addicted to the well turned-out grid, and convoy vehicle procedure is included in the study’s end.

摘要

电动汽车的使用必须成为未来交通工具的一部分。电力驱动缺陷的检测、评估和场景可提高电动汽车 (EV) 的可信度。永磁同步电机 (PMSM) 驱动器具有多种用途，适合其增强的战术灵活性、出色的控制厚度和更高的效率。在本次学习中，使用在 MATLAB/Simulink 中构建的快速数字双胞胎 (i-DT) 来构建 PMSM 监控系统和预测。一个人工神经系统（ANN）除了模糊逻辑被用来映射源范围，EV出行的时间点，并输出外部温度，扭曲的热度，时刻来填充性能润滑剂，和负责磁场的划分减弱，以确定永磁体 (PM) 的持续建设性寿命 (LCL)。这是在链接车辆的背景下进行的，并作为云计算、交通数据的可能优势的例证和智能交通系统 (ITS) 可以提供增强的 PHEV 能源管理。该研究的最后包括对优化算法进展、开发标准、PHEV 混合依赖于良好的网格和车队车辆程序的未来进展的趋势分析。