使用車載資通訊之電動車智慧節能行駛技術之研究

Research on Telematics Enabled Intelligent Eco-driving Technology for Electric Vehicles

周芳杰(FANG-CHIEH CHOU)

指導教授 ： 李綱

國立臺灣大學/工學院/機械工程學研究所/碩士(2013年)

https://doi.org/10.6342/NTU.2013.00593

摘要

車載資通訊技術過去多半僅限於車內影音娛樂、導航與電子收費等應用系統之開發，這類系統多半與車輛中控、安全及動力系統沒有直接的關聯性，系統整合與開發難度較低。近年來由於車載資通訊技術的快速發展，其應用領域已逐漸拓展至開發難度更高，且與車輛控制相關的安全性、節能性及便利性提升等應用目的之系統，例如結合車間無線通訊、感測及控制技術所開發之偕同式適應性巡航控制(Cooperative Adaptive Cruise Control, CACC)系統。本論文之研究則探討如何使用車載資通訊技術，以期能開發提升電動車能源效率之技術，藉以增加電動車的續航力。
本論文提出兩類提升電動車續航力的方法:第一種方法是以駕駛者輔助系統(Driver Assistace System)提供電動車駕駛人符合節能行駛目的之車速建議，再由駕駛者自行控制車速，系統並不直接介入車輛的操控;第二種方法則是透過車輛中控系統直接介入電動車馬達動力系統之控制，藉由控制動力系統馬達於最佳能效操作點之方式，達到提升電動車能效之目的。本論文將探討如何利用車載資通訊所取得之即時交通資訊與道路參數，應用於上述兩類電動車智慧節能系統之開發。
在本研究的第一部份是開發節能行駛駕駛輔助系統，此系統稱為綠色控制單元(Green Control Unit, GCU)，此系統是以一輛使用感應馬達提供驅動力之電動車作為研究載具，其軟體開發是採用模型預測控制(Model Predictive Control)理論，將電動車節能行車控制視為一個包含拘束條件(constraints)之最佳化問題，然而其預測區間(prediction horizon)並非以時間為單位，而是以一維空間(距離)為單位，所使用之模型包含車輛縱向動力學及感應馬達能耗模型，拘束條件則是包括前方道路坡度、地面最大靜摩擦力及速限等資料，此類資料假定可透過車載資通訊技術取得並進行即時更新，藉以計算出符合節能行駛、安全性等多重目的之最佳行車速度，供駕駛者參考。本研究透過電腦模擬的方式，分析比較不同MPC為基礎之GCU演算法之節能效益差異，並探討道路參數及模型參數對於節能效益之影響。
本研究的第二部分是開發使用車載資通訊之電動車智慧中控系統，該系統是以一輛開發中之複合動力電動車作為研究載具，該車前軸使用一套牽引馬達(traction motor)動力系統，後軸則裝置兩套輪內馬達(in-wheel motor)動力系統以提供電動車驅動力，前者使用一具40kW感應馬達，後者使用兩具28kW永磁同步馬達。此兩類馬達動力系統之性能與能耗特性存在互補性，前者能源效率在低速、高扭力下較後者高，故較適合在低速下或爬坡時提供驅動力，後者則適合於高速下提供驅動力。本研究所開發之智慧中控系統可根據複合動力電動車當下之操控狀態與道路參數進行動力分配之最佳控制，並提出兩種控制策略，一是採用瞬時功率最小化方法，另一種則是採用模型預測控制技術。這兩類動力分配控制策略再與其他控制方法，例如:動態規劃法(Dynamic Programming)及平均動力輸出等方式，利用電腦模擬進行比較，模擬所使用之馬達模型則是根據真實馬達動力系統測試數據所建立。模擬結果顯示所提出之兩種動力分配控制策略皆可使複合動力電動車之能源效率高於單獨使用感應馬達之電動車，且控制演算法皆具備即時性。

關鍵字

車載資通訊 ； 電動車 ； 複合式動力電動車 ； 節能行駛 ； 駕駛輔助系統 ； 車輛控制系統 ； 模型預測控制 ； 最佳化

並列摘要

The telematics was usually adopted for more straightforward applications such as video and audio amusement, electric toll collection and navigation etc., which are not usually coupled with the vehicle control unit (VCU), safety and power control systems. In recent years, its usage have been extended to more sophisticated applications involved with VCU due to the development of telematics, such as cooperative adaptive cruise control (CACC) which integrates vehicle to vehicle communication, sensor technique and control technique. In this research, a study on incorporating telematics to improve driving efficiency and extend the electric vehicle driving range is carried out.
Two methods to extend the electric vehicle driving range have been proposed in this thesis. The first approach is the driver assistance system (DAS) which can improve driving efficiency by providing suggested velocity profile for driver. The driving range of the EV could be improved if the driver follows its suggestion. In another approach, the powertrain is controlled directly by the VCU, the energy efficiency is improved by operating the motors at high efficiency operation point. In both parts, the method and impact of incorporating the real-time traffic information and road condition is studied.
The DAS which is called the green control unit (GCU) developed in this research is deployed on an electric vehicle driven by an induction motor. The model predictive control (MPC) theory which formulates the eco-driving strategy as an optimization problem with constraints is adopted in the development of the GCU. The GCU is able to provide a velocity suggestion that considers both energy efficiency and safety for the EV driver. In GCU, the prediction model includes vehicle longitudinal dynamics and induction motor loss model. The altitude profile ahead, speed limit and friction coefficient that can be updated via telematics are considered as constraints in the MPC formulation. The simulation is carried out and the GCU performances with different MPC formulation are compared. The impacts of parameter accuracy and information availability are also investigated.
The second part of the research is focused on developing intelligent control unit that utilizes telematics on the EV with multiple traction motors. The EV with multiple traction motors includes one front wheel driving traction motor which is a 40kW induction motor and two rear wheel driving in-wheel motors which are two 28kW permanent magnet synchronous motors (PMSM). The idea to improve energy efficiency is that the efficiency characteristics of these two motors are able to complement each other. The front traction motor provides traction force and is suitable for when the vehicle is driven at low speed but with high torque demand, due to the fact that induction motor demonstrates higher efficiency in the aforementioned operating region. The rear permanent magnet synchronous motors are suitable to provide traction force while vehicle is driven at high speed. In this work, two distribution strategies which distribute different traction force ratio based on current vehicle states are proposed to improve energy efficiency. The first method is the instantaneous distribution strategy and the second is the model predictive control based technique. These two distribution strategy are assessed by comparing them with other distribution strategies such as dynamic programming and even distribution strategy. The simulation is carried out with motor efficiency maps measured by experiment. The results show that both strategies developed in this research is able to improve the energy efficiency for the compound electric vehicle.

並列關鍵字

Telematics ； Electric vehicle ； EV with multiple traction motors ； Eco-driving ； Driver assistance system ； Vehicle control unit ； Model predictive control ； Optimization

參考文獻

[39] 石穎哲, "應用粒子群最佳化之複合動力電動車節能動力分配即時策略," 碩士論文, 機械工程學研究所, 國立臺灣大學, 台北, 臺灣, 2012.

[4] M. Barth and K. Boriboonsomsin, "Energy and Emissions Impacts of a Freeway-based Dynamic Eco-driving System," Transportation Research Part D: Transport and Environment, vol. 14, pp. 400-410, 2009.

[5] A. A. Malikopoulos and J. P. Aguilar, "An Optimization Framework for Driver Feedback Systems," Intelligent Transportation Systems, IEEE Transactions on, vol. 14, pp. 955-964, 2013.

[6] S. Mandava, K. Boriboonsomsin, and M. Barth, "Arterial Velocity Planning Based on Traffic Signal Information under Light Traffic Conditions," in 12th International IEEE Conference on Intelligent Transportation Systems, 2009, pp. 1-6.

[7] N. J. Kohut, J. K. Hedrick, and F. Borrelli, "Integrating Traffic Data and Model Predictive Control to Improve Fuel Economy," presented at the 12th IFAC Symposium on Transportation Systems, 2009.