臺灣博碩士論文加值系統

騎乘電動助力車仍需踩踏腳踏板來驅使車子前進，不同的是，該車配置一助力馬達，在踩踏過程中馬達會提供些許助力，進而提升騎乘的舒適性。一般騎乘時遇到的路況類別不外乎坡度、風阻、和路面摩擦阻力，本文將提出一套行車坡度偵測系統。然而，不同的路況，馬達的助力或換檔時機必須有所調整，因此，如果有一套及時的坡度偵測系統，將有助於廠商設計出舒適性和安全性兼顧的電動助力車。

本系統讀取電動車的行車資訊，經微控器運算後，對路況做出即時的判別，其資訊源包括電池保護板直流匯流排的電流、和控制馬達轉動的電壓訊號。微控器內部程式的設計依據路況、電流、和馬達輸出轉矩三者的關係來實現，詳細的描述在本文第二章至第四章呈現。最後欲確認本系統是否可行，選擇坡度為路況因子進行實驗，原因是不同坡度的路況其狀態穩定且容易找尋。實測分成兩部分，第一部分驗證對路況的判別結果是否準確；第二部分為觀察響應時間是否在可接受範圍內；而實驗後的結果顯示，本系統比該電動車的坡度偵測計效果更好，實驗部分在第五章討論。

We still need to pedal when riding an e-bike, but the differ¬ence is that there is a DC motor in the front wheel. During pedaling, the rider gets addi¬tional power from the motor, and that will enhance the comfort of pedaling. Gener¬ally speaking, categories of various resistant torques are nothing else but slope, wind resistance, and road friction. However, the power of motor and the timing of shifting gear have to adjust to different resistances. This thesis presents a system which estimates vari¬ous slope based on an e-bike and it will be useful for e-bike manufacturers.

During riding, the MCU will do a real-time discrimination by computing data which includes the DC bus current of Current-protection and the voltage signal for controlling motor. Some programs will be designed according to the relationship between the resistance, the current, and the torque of DC motor. The above is described in chapter 2 and chapter 3. Finally, we will check whether the system is feasible, so we choose "slope" which is the factor of experiment. This factor is more stable and can be found easier than oth¬ers. There are two experiments, one is the accuracy of discrimination and the other is about response time. However, we will say that this system is better than the slope sensor on the e-bike in chapter 4.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 文獻回顧 3
1.3.1 路況對騎乘的影響 3
1.3.2 路況和馬達的關係 4
1.4 論文章節概述 5
第2章 硬體介紹 6
2.1 微控器 7
2.2 助力馬達 9
2.3 電池保護電路 10
2.4 G Sensor 12
2.5 Wheel Sensor 13
2.6 Current Sensor 14
2.7 硬體作動流程 18
第3章 理論推導及實現坡度偵測 19
3.1 馬達數學模型 19
3.1.1 建立模型與模擬 19
3.1.2馬達特性曲線 24
3.2 線性迴歸 26
3.2.1 複迴歸模型 26
3.2.2 變異數分析 27
3.2.3 F-test 29
3.2.4 R^2(R square) 30
3.2.5 〖adj_R〗^2(adjusted R square) 30
3.2.6 Summary of ANOVA 31
3.3 實現路況偵測 32
3.3.1 坡度與馬達的關係 32
3.3.2 坡度判別策略 35
第4章 實驗結果 38
4.1 坡度判別結果 38
4.2 響應時間比較 41
第5章 結論與未來展望 43
5.1 結論 43
5.2 未來展望 43
參考文獻 46
附錄 48
附錄A 加速度計算 48
附錄B 電流和V_com計算 51
附錄C 行車紀錄器 54

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