Design and development of a high-efficiency electric drive train for hybrid electric vehicle applications

众所周知，现代混合动力电动汽车（HEV）的主要成本发生在开发和管理能量存储系统和车载发电方面。节约混合动力汽车运行的一个有效途径是设计一个最佳效率的电驱动系统。考虑到这一事实，该项目的主要目标是双重的。第一个目标是在典型的城市和高速公路驾驶时间表上对值得注意的DC/AC牵引逆变器和电机损耗（效率）进行建模。一个无刷直流（BLDC）电机将建模和设计的推进。这就引出了该项目的后续关键目标，包括设计和实施一种新型的混合动力控制策略，以最大限度地提高电动传动系统的效率。在项目的第一部分中总结的基础计算和估计的电动传动系统效率，为HEV系统级控制领域的开发工作奠定了坚实的基础。基于这些知识，一个新的控制策略将被设计，其主要目标将是最终最大限度地提高电力推进系统的效率。混合动力汽车的控制策略将基本上使用电动机来满足车辆的动力需求，并保持电池内的特定电荷。核心挑战将是找到最佳的运行区域（从速度-扭矩运行数据），控制最大的电机效率。一些关键控制参数可以包括电池充电状态（SOC）、全电驱动能力和最佳混合因子（HF）。因此，关键的实验将是研究各种控制变量的最佳组合，以最大限度地提高系统效率。总之，在本项目中提出的深入的效率建模和设计实施，基于一种新的混合动力汽车控制策略的发展，将支持电动传动系统的最大效率水平的升级，为未来先进的车辆应用。

It is a well known fact that the major cost in modern hybrid electric vehicles (HEV) is incurred in developing and managing the energy storage system and on-board power generation. An effective way to economise HEV operation is to design an optimally efficient electric drive train. Keeping this fact in mind, the major goals of this project are two-fold. The first goal is to model the noteworthy DC/AC traction inverter and motor losses (efficiencies) over typical city and highway driving schedules. A brush-less DC (BLDC) motor will be modeled and designed for propulsion. This leads to the subsequent key target of the proposed project, which includes the design and implementation of a novel hybrid control strategy, in order to maximize the electric drive train efficiency.The groundwork calculations and estimated electric drive train efficiencies, summarized in the first part of the project, form a solid basis for development work in the area of HEV system-level control. Based on this knowledge, a novel control strategy will be designed, the principal objective of which would be to eventually maximize the efficiency of the electric propulsion system. The HEV control strategy will essentially use the motor for fulfilling vehicular power demands and maintaining a specific charge within the batteries. The core challenge would be to find the most optimal operating region (from the speed-torque operating data) that governs maximum motor efficiency. Some of the critical control parameters could include, battery state-of-charge (SOC), all-electric driving capability, and the optimal hybridization factor (HF). Therefore, the key experiment would be to investigate the best possible combination of various control variables, in order to maximize system efficiency.In summary, the in-depth efficiency modeling and design implementation proposed in this project, based on the development of a novel HEV control strategy, will support the escalation of maximum efficiency levels of electric drive trains for future advanced vehicle applications.