| 研究生: |
謝一民 Hsieh, Yi-Min |
|---|---|
| 論文名稱: |
插電式混合動力車輛之能量管理控制策略 Power Management Strategy for Plug-in Hybrid Electric Vehicle |
| 指導教授: |
劉彥辰
Liu, Yen-Chen |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 機械工程學系 Department of Mechanical Engineering |
| 論文出版年: | 2016 |
| 畢業學年度: | 104 |
| 語文別: | 中文 |
| 論文頁數: | 112 |
| 中文關鍵詞: | 插電式混合動力車輛 、能量管理控制策略 、模型預測控制 |
| 外文關鍵詞: | Plug-in hybrid electric vehicle, power management control strategy, model predictive control |
| 相關次數: | 點閱:123 下載:8 |
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插電式混合動力車(Plug-in Hybrid Electric Vehicle, PHEV)為新型態之油混合動力車輛,與普通混合動力車輛(Hybrid Electric Vehicle, HEV)相比,插電式油電混合車具有較大之馬達動力和電池容量。因此,插電式混合動力車之動力 來源通常以馬達為主,再輔以燃油引擎進行長距離且較低油耗之駕駛。油電混 合車之動力控制系統主要用於內燃機與馬達之動力配置,其功能在使車輛行駛 於不同狀況下,能依照環境與車輛本身之相關參數,提出合乎需求之混合動力 匹配,以確保車輛之駕駛性能與能量節約。為了讓插電式混合動力車輛在不同環境與條件之下,具有最佳的性能和油耗,混合動力控制策略成為一個重要的因素。因此,本論文對插電式混合動力車輛制定能量管理控制策略,探討在不同的操作環境與行車狀態下,對整體車輛動態系統進行模擬與分析;並提出不同狀態下之動力混合控制策略,進而對各參數所造成的影響進行評估,以改善車輛之性能並達到節省能源之目的。
本論文使用ADVISOR與Matlab/Simulink模擬軟體進行模擬分析,探討在美國國家環境保護局之行車狀態Urban Dynamometer Driving Schedule (UDDS)以及歐盟之行車狀態New European Driving Cycle (NEDC)下,控制策略之性能。針對並聯式架構之插電式混合動力車,提出基於引擎啟動門檻控制策略以及模型預測控制策略,能夠在已知行程距離與時間的情況下使用。基於引擎啟動門檻控制策略,本論文首先藉由所剩距離與所剩電池電量以比例控制來調整引擎啟動門檻,使插電式混合動力車輛能夠在一段行程中完全使用Charging-depleting (CD)模式,來達到降低油耗之效果。隨後,本研究利用模型預測能量管理控制策略,藉由模型預測控制與動態規劃控制引擎啟動,並改善在一段時間內的油耗。模擬結果顯示,所提出之模型預測控制策略,整體行程油耗都比其他策略要好。本控制策略同時使用指數變化與類神經網路預測速度,以供模型預測控制使用,使本研究更貼近真實情況,預測速度之模擬結果也與完全已知行程資訊相差不多。此外也提出了以類神經網路調整成本函數之方法,能夠藉由行車狀態之資訊來決定成本函數之權重,使模型預測控制策略較能在現實生活中使用。
Plug-in hybrid electric vehicle (PHEV) is a kind of hybrid electric vehicles (HEV) that has a large capacity battery which satis es the requirement of the distance for commuters. The parallel PHEV has two kinds of power source, engine and motor. To have a good fuel economy, a control strategy for spilt the power between the engine and the motor is important. In this thesis, an engine-on power threshold based control strategy and a model predictive control (MPC) based power management strategy are proposed for a parallel PHEV. The engine-on power threshold based control strategy trades o the distance and the state of charge (SOC) by a proportional controller to let PHEV operate in Charging-depleting (CD) mode which can improve the fuel economy of PHEV. By MPC and dynamic programming (DP), the MPC based power management strategy can also let PHEV operate in Charging-depleting (CD) mode. MPC is used to predict the system performance of PHEV, and the system performance is optimized by DP. Both strategy can improve the fuel economy of PHEV compared by the normal strategy|CD-CS strategy. In this thesis, methods of velocity prediction and parameter tuning are also proposed. Exponentially varying and neural network (NN) are both used to predict velocity of PHEV, which makes the MPC strategy more real. NN is also used to tune the parameter of the cost function. The strategies also have the potential for real time implement.
[1] Z. Chen, C. C. Mi, J. Xu, X. Gong, and C. You. Energy management for a
power-split plug-in hybrid electric vehicle based on dynamic programming and
neural networks. Vehicular Technology, IEEE Transactions on, 63(4):1567{
1580, May 2014.
[2] Q. Gong, Y. Li, and Z. R. Peng. Trip-based optimal power management of
plug-in hybrid electric vehicles. Vehicular Technology, IEEE Transactions on,
57(6):3393{3401, Nov. 2008.
[3] G. Wu, K. Boriboonsomsin, and M. J. Barth. Development and evaluation of
an intelligent energy-management strategy for plug-in hybrid electric vehicles.
Intelligent Transportation Systems, IEEE Transactions on, 15(3):1091{1100,
June 2014.
[4] T. Feng, L. Yang, Q. Gu, Y. Hu, T. Yang, and B. Yan. A supervisory
control strategy for plug-in hybrid electric vehicles based on energy demand
prediction and route preview. Vehicular Technology, IEEE Transactions on,
64(5):1691{1700, May 2015.
[5] 朱峻賢. 2015年全球車市概況. 財團法人車輛測試中心, 2016.
[6] U.S. Energy Information Administration (EIA). U.s. energy-related carbon
dioxide emissions, 2014. Technical report, U.S. Energy Information Administration
(EIA), Nov. 2015.
[7] National Oceanic and Atmospheric Administration (NOAA) Earth System
Research Laboratory. Full mauna loa co2 record, June 2016.
[8] S. G. Wirasingha and A. Emadi. Classi cation and review of control strategies
for plug-in hybrid electric vehicles,. Vehicular Technology, IEEE Transactions
on, 60(1):111{122, Jan. 2011.
[9] Z. Chen, C. C. Mi, B. Xia, and C. You. Energy management of power-split
plug-in hybrid electric vehicles based on simulated annealing and pontryagin's
minimum principle. Journal of Power Sources, 272:160{168, Dec. 2014.
102
[10] Z. Chen, C. C. Mi, R. Xiong, J. Xu, and C. You. Energy management of
a power-split plug-in hybrid electric vehicle based on genetic algorithm and
quadratic programming. Journal of Power Sources, 248:416{426, Feb. 2014.
[11] S. Stockar, V. Marano, M. Canova, G. Rizzoni, and L. Guzzella. Energyoptimal
control of plug-in hybrid electric vehicles for real-world driving cycles.
Vehicular Technology, IEEE Transactions on, 60(7):2949{2962, Sep. 2011.
[12] B. Zhang, C. C. Mi, and M. Zhang. Charge-depleting control strategies and
fuel optimization of blended-mode plug-in hybrid electric vehicles. Vehicular
Technology, IEEE Transactions on, 60(4):1516{1525, May 2011.
[13] M. Zhang, Y. Yang, and C. Mi. Analytical approach for the power management
of blended-mode plug-in hybrid electric vehicles. Vehicular Technology,
IEEE Transactions on, 61(4):1554{1566, May 2012.
[14] C. Zhang and A. Vahidi. Route preview in energy management of plug-in hybrid
vehicles. Control Systems Technology, IEEE Transactions on, 20(2):546{
553, Mar. 2012.
[15] S. Cordiner, M. Galeotti, V. Mulone, M. Nobile, and V. Rocco. Trip-based
soc management for a plugin hybrid electric vehicle,. Applied Energy, Jul.
2015.
[16] L. Wang. Model Predictive Control System Design and Implementation Using
MATLAB. Springer, 2009.
[17] H. A. Borhan, A. Vahidi, A. M. Phillips, L. Kuang, and I. V. Kolmanovsky.
Predictive energy management of a power-split hybrid electric vehicle. In
American Control Conference, 2009. ACC '09., pages 3970{3976, June 2009.
[18] A. Taghavipour, N. L. Azad, and J. McPhee. An optimal power management
strategy for power split plug-in hybrid electric vehicles. International Journal
of Vehicle Design, 60(3/4):286{304, 2012.
[19] M. Vajedi, A. Taghavipour, N. L. Azad, and J. McPhee. A comparative
analysis of route-based power management strategies for real-time application
in plug-in hybrid electric vehicles,. In American Control Conference (ACC),
2014., pages 2612{2617, June 2014.
[20] K. Yu, Q. Liang, J. Yang, and Y. Gong. Model predictive control for hybrid
electric vehicle platooning using route information. Journal of Automobile
Engineering, 2015.
103
[21] H. Borhan, A. Vahidi, A. M. Phillips, M. Kuang, I. V. Kolmanovsky, and
S. D. Cairano. Mpc-based energy management of a power-split hybrid electric
vehicle. Control Systems Technology, IEEE Transactions on, 20(3):593{603,
May 2012.
[22] J. Zhang and T. Shen. Nonlinear mpc-based power-assist scheme of internal
combustion engines in plug-in hybrid electric vehicles. In Control Conference
(ECC), 2014 European, pages 1164{1169, June 2014.
[23] J. Zhang, T. Shen, T. Sawada, and M. Kubo. Nonlinear mpc-based power
management strategy for plug-in parallel hybrid electrical vehicles. In Control
Conference (CCC), 2014 33rd Chinese, pages 280{284, July 2014.
[24] C. Sun, X. Hu, S. J. Moura, and F. Sun. Velocity predictors for predictive
energy management in hybrid electric vehicles. Control Systems Technology,
IEEE Transactions on, 23(3):1197{1204, May 2015.
[25] National Renewable Energy Laboratory. ADVISOR Documentation, 2003.
[26] S. Onori and L. Serrao. On adaptive-ecms strategies for hybrid electric vehicles.
In International Scienti c Conference on Hybrid and Electric Vehicles,
Dec. 2011.
[27] S. Onori, L. Serrao, and Rizzoni G. Adaptive equivalent consumption minimization
strategy for hybrid electric vehicles. In ASME 2010 Dynamic Sys-
tems and Control Conference, pages 499{505, Sep. 2010.
[28] P. Khayyer, J. Wollaeger, S. Onori, V. Marao, U. Ozguner, and G. Rizzoni.
Analysis of impact factors for plug-in hybrid electric vehicles energy management.
In 2012 15th International IEEE Conference on Intelligent Transporta-
tion Systems, pages 1061{1066, Sep. 2012.
[29] 張斐章與張麗秋. 類神經網路. 東華書局, 1995.