近年來，由於人們生活方式改變，生活水平越來越高，石油的消耗速度也呈現指數上升，根據BP Statistical Review 專業單位之估算，目前消耗石油的速度，大約只剩下36~43年的蘊藏量，也就是說在未來的20年內，必須開發可供人類永續發展的新能源。在這之前，要如何有效地降低石油使用量，成了值得研究的目標。然而，目前最普遍的石油消耗族群，莫過於行駛在平面道路上的汽機車，所以要降低石油的使用量，最有效的方法就是改進人們普遍所使用的交通工具。有鑑於此，以馬達驅動取代傳統內燃機引擎，減低石油消耗，發展電動汽機車，似乎已成了全球共同努力的目標。然而，其續航力無法與現行搭載內燃機引擎之汽機車所比擬，是阻礙其發展的重要因素之一。所以如何增加現行電動車之續航力，成了值得研究的方向。
一般傳統式的煞車，是將行駛中的動能藉由摩擦的方式以熱能消耗掉，對整體續航力來說，並無提升的幫助。然而發展再生制動煞車，即是將行駛中的動能，藉由馬達的反電動勢，並以直流無刷馬達內部線圈作為電感器，透過微控制器控制變頻器之晶體切換順序與時機，轉換成電能，回充至電池端；同時此過程也將產生ㄧ反向力矩使馬達減速，在不需要於原驅動器添加任何元件之狀況下，達到同時煞車與增加續航力的目標。
本論文的研究核心，除了「增加電動車之續航力」之外，另一個研究重點就是「達成精確的回充電流控制」。煞車力道的強弱取決於實際回充電流的大小，因此本論文提出一個自動調整之模糊類神經網路演算法應用於電動車馬達電流控制，並利用最深梯度法和倒傳遞法，調整模糊類神經網路之參數，使得電流誤差降到最小，來實現精確的回充電流控制。此系統採Lyapunov理論推導模糊類神經網路之收斂性，以確定所提出之方法為穩定。
最後，以Microchip所生產的dsPIC30f2010做為馬達的微控制器，並利用動力機與慣性機構平台，模擬實際騎乘之狀況，驗證本論文所提出之「再生式煞車電流控制法」實際上具有所設計之特性。並將此系統裝設於電動機車上，由實測數據證明所提出之方法確實能將馬達煞車時之動能轉換為電能回充於電池端，達成再生煞車電流控制之目的。

Recently, since people changing their live style in many aspects, the living standard is as higher as possible and the gasoline is greatly consumed. According to statistic from BP Statistical Review, the index of reserved gasoline can only provide about 36~43 years. By the reason, human must develop new energy source which is sustainable and less environmental pollution in following decade. At this time, many researches are working on decreasing the gasoline-consuming efficiently to energy-saving. Since vehicles consumed numerously gasoline, improve these vehicles to energy-saving is important. Electric vehicle (EV) replaced internal combustion engine (ICE) by electric motors is more popular in the world. However, EVs are hard to popularize since the sustainability is lower than it with internal combustion engine. This defect is unacceptable for user to buy or use EVs widely. Therefore, increase the sustainability of EV is a novel research topic.
In generally, the conventional brake applies the friction to decrease the vehicle’s speed, and translate the kinetic energy to heat. The energy is just consumed. In order to recycle the kinetic energy in braking process, regenerative brake method is presented. Regenerative brake translates the kinetic energy to electric energy by utilizing the motor’s back-EMF and internal winding. The method controls the switch sequences of MOSFETs to elevate the back-EMF voltage and recharge the battery. In the meanwhile, the motors operate in braking mode, and produce an inverse torque to reduce the motor’s speed. Depend on the regenerative brake method, the motors drive can accomplish brake function and extending traveling distance without adding any component.
“Extending the traveling distance” and “Control the regenerative current” are the topics of this thesis. Since the brake torque is depended on the recharge current, a current control in EVs with self-tuning fuzzy neural network algorithm is proposed. The gradient descent and back-propagation are used to adjust the parameters of fuzzy neural network, and minimize the current error. Finally, Lyapunov’s stability theorem is applied to prove the system stability.
Finally, the microprocessors dsPIC30f2010 by Microchip, are employed to implement the proposed control algorithms. The driving and braking mode are tested on a motive machine and inertial instrument. Moreover, the implemental results of the proposed system set up on EVs demonstrate the ability of recharge and the feasibility of regenerative brake.

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