本文主要發展智慧型混合動力的驅動控制系統以應用於增程式的電動車，解決電動車電池 模組儲存能量受限制的問題，有效增加電動車的續航力。建立電動車混合動力的實驗平台，硬 體方面包括鋰電池模組、增程引擎與發電機系統、永磁式同步無刷馬達驅動系統和渦電流負載 系統，針對馬達在各種不同負載和轉速下，測試此混合動力系統的功率輸出和效率，以提供整 車能量管理系統參考，所設計的永磁式直流無刷馬達功率輸出為5.5KW/DC310V/4100RPM。 另外，本文發展一具學習能力的區間第二型類神經模糊網路系統（Interval type-2 neural fuzzy network, IT2NFN)並推導網路參數的適應性法則，使IT2NFN能線上估測電動車的總合不確定 量，以提供給控制器補償電動車在路面行駛時所遭受的各種負荷和外力對馬達驅動系統的干 擾，完成電動車速度精確穩定控制的目的。

This paper develops an intelligent hybrid power system (IHPS ) for extended-range electric cars. The IHPS solves the energy storage limitations of the battery pack and effectively increases the traveling distance of the electric car. This study established a hybrid power experimental platform that included a lithium battery module, an extended-range engine and a generator system, a permanent magnet synchronous brushless motor drive system, and an eddy current load system. Power output and efficiency tests of IHPS were run under various loads and speeds of the electric car motor. A permanent magnet synchronous brushless motor was designed with 5.5KW/DC310V/4100RPM. A motor vector control system was established using sinusoidal space vector pulse-width-modulation for inverter three-phase IGBT switching. Moreover, an interval type-2 neural fuzzy network (IT2NFN) with parameter adaptation laws was established for conducting online estimation of overall uncertainties from the variable loads and external disturbances. A robust controller based on IT2NFN was designed to ensure precise speed control for the electric car, which demonstrated favorable driving performance. Finally, simulations and experiments were performed using the designed hybrid experimental platform to demonstrate the effectiveness of the proposed control methodology.