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

众所周知，现代混合动力汽车(HEV)的主要成本是开发和管理储能系统和车载发电。节约混合动力汽车运行的一个有效途径是设计一种效率最高的电动传动系统。牢记这一事实，这个项目的主要目标有两个。第一个目标是对典型的城市和高速公路行驶计划中值得注意的DC/AC牵引逆变器和电机损耗(效率)进行建模。无刷直流(BLDC)电机将被建模和设计用于推进。这导致了拟议项目的后续关键目标，其中包括设计和实施一种新的混合控制策略，以最大限度地提高电驱动列车的效率。项目第一部分总结的基础计算和估计电驱动列车效率，为混合动力汽车系统级控制领域的开发工作奠定了坚实的基础。在此基础上，将设计一种新的控制策略，其主要目标将是最终最大化电力推进系统的效率。混合动力汽车的控制策略基本上将使用电机来满足车辆的动力需求，并在电池中保持特定的充电。核心挑战将是找到控制最大电机效率的最优运行区域(从转速-扭矩运行数据)。一些关键的控制参数可能包括电池的荷电状态(SOC)、全电动驱动能力和最佳杂交系数(HF)。因此，关键的实验将是研究各种控制变量的最佳组合，以实现系统效率的最大化。综上所述，本项目提出的深入的效率建模和设计实现，基于新的混合动力汽车控制策略的开发，将支持未来先进车辆应用的电动传动系统最大效率水平的提升。

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.