外文翻譯---基于advisor的混合動力車輛動力系統(tǒng)的開發(fā)

1、武漢科技大學(xué)本科畢業(yè)論文外文翻譯Using an Advanced Vehicle Simulator (ADVISOR) to Guide Hybrid Vehicle Propulsion System DevelopmentKeith B. Wipke，Matthew R. CuddyNational Renewable Energy Laboratory，Golden CO基于advisor的混合動力車輛動力系統(tǒng)的開發(fā)基思B. Wipke ，馬修R. Cuddy美國國家再生能源實驗室, 科羅拉多州戈爾登 摘要：全國可再造能源實驗室開發(fā)了一種名為ADVISOR的先進的汽車模型仿真軟件，用

2、來對汽車進行系統(tǒng)分析和交易研究。由于ADVISOR的快速的執(zhí)行速度和MATLAB/Simulink的開放程序環(huán)境，這個仿真系統(tǒng)非常地適用于合乎新一代汽車合作伙伴計劃PNGV要求的，高燃油經(jīng)濟性車輛的參數(shù)設(shè)計和研究。配置五種不同汽車模型，包括3種輕量級車(并聯(lián)型、串聯(lián)型和傳統(tǒng)型)與1996年車重量的2輛車一起(平行和常規(guī)傳動)。 還有在1996年加入的2種重型車（并聯(lián)型和傳統(tǒng)型）。對每類車的重要燃料經(jīng)濟敏感性參數(shù)進行分析，并且車的行駛工況參數(shù)進行設(shè)置。通過分析這些車的仿真結(jié)果，來獨立的分析串聯(lián)和并聯(lián)兩種混合動力方式對汽車的影響。先進的汽車仿真模型：ADVISOR 1994年11月，國家可再生能源

3、實驗室的交通技術(shù)中心在Mathworks公司的面向?qū)ο蟮木幊陶Z言的MATLABSimulink環(huán)境下創(chuàng)造了進的車輛模型仿真系統(tǒng)。這個模型是在汽車工業(yè)的能源部的支持下建立的。ADVISOR可以將汽車各個部件那些不連續(xù)的動作近似地看作為連續(xù)動作狀態(tài)下的穩(wěn)定狀態(tài)。那樣，在每個時刻都將忽略掉電流、電壓、轉(zhuǎn)矩、轉(zhuǎn)速的改變對系統(tǒng)的影響。這樣通過測試動力傳動系統(tǒng)在一個固定的轉(zhuǎn)矩和轉(zhuǎn)速下，在每單位時間步長下對電力的需求，允許有效率或功率的損耗。在SimulinkMatlab的環(huán)境下利用這個模型有一個非常顯著的優(yōu)勢，那就是靈活容易地來改變模型，例如可以更換一種控制策略或是更換一種再生制動算法。MATLAB也可以

4、容易的分析仿真結(jié)果，盡可能詳細地分析車輛的配置。 ADVISOR是由輸入一些車輛行駛的參數(shù)來去驅(qū)動的，包含一些經(jīng)典的特性曲線，如美國的城市循環(huán)工況，或者是速度與爬坡能力的特性曲線。在給定驅(qū)動的目標配置文件后，ADVISOR將采取向后的方式從輪轂和各動力源間各部分所需車輪速度要求的扭矩和速度來控制，這取決于混合動力單元或由電池供電。圖1顯示了一系列高級的混合模型的數(shù)值仿真。圖1 高級的混合模型的數(shù)值仿真。仿真和交互作用的檢驗是重要的重要之處是建立模型的可信度。通過與那些成功制造和測試混合動力汽車大學(xué)的合作，國家再生能源實驗室獲得了一些驗證組件模型，包含了量化的不確定性，并增加了可信的數(shù)據(jù)。最后車

5、輛幾倍的驗證，包含詳細的不確定性分析在1996年的九月完成。同時，與公共汽車模型相關(guān)的部分已完成，才外還加入了一些汽車行業(yè)中的專用模型。在此基礎(chǔ)上比較，ADVISOR看來是在根據(jù)多數(shù)模型之內(nèi)相同輸入的2%以內(nèi)。 因此，在ADVISOR的結(jié)果是通過它的算法來反映最小的不確定性；輸入數(shù)據(jù)的不確定性是分析不確定性的要原因。所以，所有輸入數(shù)據(jù)的來源模仿的在分析下面指定。車輛模型和假設(shè)：五種不同的車輛配置建模。串、并聯(lián)混合動力車以小的質(zhì)量和高效的傳動系統(tǒng)建模是為了得到像PNGV一樣的混合動力車,獲得城市高速路的燃油經(jīng)濟性在80英里加侖。這些被稱為“3倍”的車,是因為他們得到3倍于傳統(tǒng)車輛的燃油經(jīng)濟性城市

6、高速路26.6英里加侖。第三個配置是創(chuàng)建一個沒有混合動力的常規(guī)的車輛。第四和第五車輛配置是由以傳統(tǒng)的車輛(大概在1.45倍由于柴油機和手操作的傳動系統(tǒng)),并將它變成并聯(lián)混合動力。表1提供五種車輛仿真配置的燃油經(jīng)濟性的差異,而表2給出了數(shù)據(jù)來源的輸入。表1:車輛配置模型的主要參數(shù)值：VehicleConfig3XParallelHybrid3XSeriesHybrid2.46XLightWt(nonhybrid)1.45XConv.(diesel)1.70XParallelHybridMass(kg)1000.0001000.0001000.0001611.0001611.000BatteryC

7、ap.(kWh)1.1003.700n/an/a1.800P e a kH P UPower(kW)31.00030.00047.00077.00052.000Peak MotorPower(kW)12.00041.000n/an/a20.000CDA0.4000.4000.4000.7000.700Crolling-resistance0.0080.0080.0080.0110.011City(mpg)73.80072.30056.10033.80040.700High way(mpg)94.30093.60082.10047.00052.800Combined(mpg)81.80080.5

8、0065.40038.70045.300縮放比例：因為從0-60英里/小時的加速時間和在55英里/小時和爬坡能力是所有車的性能要求，液壓動力系統(tǒng)，在這種情況下類似奧迪五缸的渦輪柴油引擎和電動機可以滿足這兩個參數(shù)目標。一個主要的假設(shè)下,這兩種結(jié)構(gòu)的轉(zhuǎn)矩/速度(相當于功率損耗效率圖)可以在坐標圖上簡單的標出扭矩比例。眾所周知,這不是最精確的方法,但被用于缺乏一個可合理尺度算法的情況下。質(zhì)量：這些傳統(tǒng)的1.45倍的和混合動力的車輛的質(zhì)量數(shù)據(jù)來源于美國技術(shù)評估局對于當時流行的福特品牌的金牛座車的評估報告。在報告中對于那些質(zhì)量大概在一噸左右的3X型的車輛會在2015年以后用鋁來代替其他的金屬作為制造的主

9、要材料。這與今天的車輛在質(zhì)量比較上是巨幅的減少；并且這些數(shù)據(jù)還可以用在其它的高效率的總成以及作為滿足PNGV計劃中所要求的技術(shù)參數(shù)。混合動力控制策略：串聯(lián)的混合控制是用一個類似于“恒溫器”的東西來操縱液壓動力裝置，當在開啟狀態(tài)時HPU是在一個固定的扭矩和轉(zhuǎn)速下運行。在這項研究中，當電池的充電量低于40%時HPU將打開，而電池的充電電量達到80%以上時將關(guān)閉。并立案的混合控制策略對于那些電池使用時間短的車有影響，除非在電池電量低的時候保持電池充電。它是樣定義的，將60%的SOC狀態(tài)定位高，將50%的SOC狀態(tài)定為低：*HPU不會停機（除非實在不需要時）*不論是否達在電池的充電狀態(tài)發(fā)動機都可以產(chǎn)生

10、在成制動力。*HPU一般會在遇到指令或是在發(fā)動機需要必要的動力時會提供動力，也有一些特殊情況：1、當電量過低時會提高扭矩來重新充電。2、當點亮過高時將不會充電。燃油經(jīng)濟性的計算： 為了證明在一個測試循環(huán)中電池組充電狀態(tài)的變化，推薦用一個簡單版本的美國汽車工程是學(xué)會的混合動力汽車測試程序。為了提高城市燃油的經(jīng)濟性，兩個緊接的從高電量狀態(tài)到低電量狀態(tài)的城市循環(huán)工況各自反映了電池電量的減少和增加。在電池充電狀態(tài)變化不大的情況下，可以用一種簡單的線性插值的方法來預(yù)測汽車燃油經(jīng)濟性的評估值。這將確保一個公平的比較常規(guī)的車輛和混合動力汽車對電池組的任何電能盈余或赤字。如果沒有這樣計算電池組的充電狀態(tài)，由于

11、電能被用來代替燃料能源，混合動了可能似乎已經(jīng)具有極高的燃油經(jīng)濟性。 表2 ： 數(shù)據(jù)的來源為模仿輸入和性能要求VehicleParameterValues UsedSource of InputDataCDA0.4,0.7 m2PNGV Goals,Moore, T.CC rolling -resistance0.008,.011OTATransmissionEfficiencies:5 spd。(parallel,conventional) / 1spd. (series)92% /98%AutomotiveEngineering,1996Heat Engine (HPU)Scaled85 k

12、WTDIDieselStock, D., 1990Motor/ControllerScaled75 kWACInductionLesster, L., 1993Energy Storage:BatteriesHorizon12N85Electro source0-60 mph time12.0secondsPNGV GoalsGrade ability at 55mph6.5%indefinitelyMore stringentthan PNGVgoal, which is6.5% for 20minutes車輛的燃油經(jīng)濟性的關(guān)鍵參數(shù)：靈敏度分析的關(guān)鍵參數(shù)的模擬車輛說明如何敏感輸出（燃油經(jīng)濟性

13、在這種情況下）是變化的輸入?yún)?shù)。這允許并排比較輸入?yún)?shù)，以便把重點放在提高燃油經(jīng)濟性的重要技術(shù)領(lǐng)域。此外，相對比較有可能的是，由于輸入?yún)?shù)的變化可以很容易地計算出燃油經(jīng)濟性的數(shù)量變化。對于五種基本模型中的每一種都可以對關(guān)鍵參數(shù)進行上下5%的調(diào)整。這表明這些系數(shù)可以在超出10%的情況下計算，但是信任值不能超出正負10%。 對于這五種汽車模型的參數(shù)分析的結(jié)果顯示在圖2的條形圖中。參照表1中的五種車的基本參數(shù)。請注意，所有五種車的靈敏度系數(shù)是1.0 。這也就是說，增加1%的引擎效率，將增加1%的燃油經(jīng)濟性。由于發(fā)動機是混合動力或常規(guī)車輛的能量轉(zhuǎn)換器，這并不奇怪，但仍然必須牢記。由于這種大型HPU的效

14、率敏感性很強，工業(yè)部和政府正將極大的努力放在研究燃氣輪機，先進的柴油機，斯特林發(fā)動機和燃料電池上。圖2：燃油經(jīng)濟性的主要參數(shù) 圖2的結(jié)果表明，電池效率和電機效率的敏感性系數(shù)為3倍串聯(lián)式車輛大致是并聯(lián)式車輛的三倍。這樣的理由是，因為所有的到車輪的驅(qū)動力，來自于串聯(lián)式電動機，高功率和高功率損耗發(fā)動機的串聯(lián)。這樣串聯(lián)式的比并聯(lián)式可以通過電池提供更多的動力，也會引起電池更多的消耗。在技術(shù)層面有一定的風險，這就是說串聯(lián)式混合動力汽車在提高發(fā)動機效率和電池方面受到的影響比并聯(lián)式的要多，如果不能通過改進來解決將遇到更大的困難。 坐標軸下的四個參數(shù)量會在燃油經(jīng)濟性提高的時候減小。我們的目標是讓這些參數(shù)盡可能的

15、維持在低水平。舉例說明盡可能減少負荷對3倍車的影響：每減少1配件負荷，燃油經(jīng)濟性有0.24的升幅。觀察負荷為800w的曲線，每降低10%將提高2.4%的燃油經(jīng)濟性。這些結(jié)果使燃油經(jīng)濟性的權(quán)衡是可以量化的附加功能的汽車，如白天行駛的時候開燈。混合動力汽車的設(shè)計空間映射參數(shù)的研究:圖3顯示了在advisor的計算了考慮表1中的3倍的并聯(lián)式混合動力車在有空氣阻力和滾動阻力情況下，HPU的平均效率和質(zhì)量的燃油經(jīng)濟性曲線?？梢缘贸?0英里每加侖的曲線是PNGV計劃中所要求的。圖3：HPU效率和裝備質(zhì)量對并聯(lián)式混合動力車燃油經(jīng)濟性的影響兩種大眾的質(zhì)量在圖3中顯示的為1000公斤和1600多公斤并且在五種車

16、輛模型中被定義。從這個圖中可以清晰顯示出對于今天傳統(tǒng)的HPU效率在20%左右的花火點火式發(fā)動機減重和減阻是不夠的。車重將減少一半以上，在近期還很難實現(xiàn)。同時，該圖顯示使3倍的混合動力車提高HPU效率，采用混合動力模式以及減阻還很難。根據(jù)這張圖，我們可以推斷，一輛3倍的混合動力車在1600公斤的裝備質(zhì)量時要求HPU的效率達到47%，遠遠超過了柴油車在這個質(zhì)量時的平均值。混合動力的作用：單獨考慮混合動力的作用，也就是用混合動力裝置來代替?zhèn)鹘y(tǒng)的動力驅(qū)動裝置，在最初的五種汽車模型中，3倍的混合動力車沒有被定為混合動力而1.45倍的被定為混合動力。由于比較表1中的燃油經(jīng)濟性，傳統(tǒng)輕質(zhì)量的汽車在達到65.

17、4英里每加侖時3倍的串聯(lián)和并聯(lián)的混合動力車將分別達到80.5英里每加侖和81.8英里每加侖。這樣傳統(tǒng)的輕質(zhì)量的車在用了混合動力裝置后保持車輛質(zhì)量不變的情況下，會將在65.4英里每加侖的時速時提高約24%。對于38.7英里每小時的傳統(tǒng)汽車，在這特定的情況下會提高17%。這表明這些車的混合動力裝置并沒達到完美的地步。我們不能將混合動力的估計值作為上限，而應(yīng)僅作為參考值。 混合動力的另一個方面的作用可以從3倍的混合動力車采取串聯(lián)式和并聯(lián)式之間的差異來獲得。對于這兩種不完美的配置，燃油經(jīng)濟性將會達到串聯(lián)式為81.8英里每加侖，并聯(lián)式為80.5英里每加侖。這就意味著，在保持質(zhì)量相同的情況下，兩種混合動力

18、的布置形式得到的燃油經(jīng)濟性幾乎相同。 一個合理的理由認為串聯(lián)式的混合動力比并聯(lián)式的混合動力需要更強和更大的電池組。如果將串聯(lián)式混合動力的車的質(zhì)量定為1100公斤，而不是最初定位的1000公斤，那么燃油經(jīng)濟性將達到76.5英里每加侖。讓我們來考慮這兩種使燃油經(jīng)濟性達到80英里每加侖的技術(shù)方法。圖4是一個二維的圖，來顯示在質(zhì)量為1100公斤時，燃油經(jīng)濟性與傳動效率的關(guān)系。圖4 ：配件負荷1100公斤條件下的串聯(lián)式混合動力的燃油經(jīng)濟性和傳動效率 由圖中1100公斤車輛基線的圓點指出，當配件的負荷在800w時看傳動效率為84.5%的等高線，對于這輛車有一些可能的方法讓燃油經(jīng)濟性回到80英里每加侖：沒減

19、少200w的配件負荷或者將傳動效率提高4%，也可以同時滿足上述兩個條件。給出這樣串聯(lián)式的混合動力模型用在這里會有更高的效率，通過交流式異步電機可以提高單齒變速器的效率到達98%，但是傳動效率會有所限制。一些謹慎的設(shè)計師會傾向于減少輔助負荷。結(jié)束語： 全國可再造能源實驗室發(fā)明了一種名為ADVISOR的先進的汽車模型仿真軟件，用來對汽車進行系統(tǒng)分析和交易研究。定義了五種車輛模型并對關(guān)鍵的參數(shù)進行計算。串聯(lián)形式和并聯(lián)形式的混合動力模式的燃油經(jīng)濟性都被設(shè)計到，包括在燃油經(jīng)濟性到達80英里每加侖的情況也被涉及到。有了這些汽車模型，燃油經(jīng)濟性回因為混合動力而提高17-24%。三倍的串聯(lián)式和并聯(lián)式的混合動力

20、車在相同的整備質(zhì)量下有相同的燃油經(jīng)濟性，但是當汽車的重量在提高100公斤是到達80英里每加侖將會非常困難。參考文獻：1. Duleep, K. G., Fuel Economy Potential of Light Duty Vehicles in 2015+, Draft Final Report, Energy and Environmental Analysis, Inc., Arlington, Virginia, April 1995.2. Efficiency Guidelines for Future Manual Transmissions, Automotive Engine

21、ering, Jan. 1996.3. Lesster, L. W., Lindberg, F. A., Young, R. M., and Hall, W. B., An Induction Motor Power Train for EVs-The Right Power at the Right Price, Advanced Components for Electric and Hybrid Electric Vehicles: Workshop Proceedings, National Technical Information Service. October 27-28, 1

22、993.4. Moore, T., Lovins, A., Vehicle Design Strategies to Meet and Exceed PNGV Goals, SAE Paper #951906, 1995.5. Office of Technology Assessment (OTA), Automotive Technologies to Improve Fuel Economy to 2015, Washington, DC, December 1994.6. Partnership for a New Generation of Vehicles, Program Pla

23、n, July, 1994.7. SAE, Draft SAE J1711, Measuring the Electric Energy Consumption, All Electric Range, Fuel Economy, and Exhaust Emissions of Hybrid Electric Vehicles, 1995.8. Stock, D., Bauder, R., The New Audi 5-Cylinder Turbo Diesel Engine: The First Passenger Car Diesel Engine with Second Generat

24、ion Direct Injection, SAE Special Publication 823, SAE Paper # 900648, 1990.外文原文：Using an Advanced Vehicle Simulator (ADVISOR) to Guide:Hybrid Vehicle Propulsion System DevelopmentKeith B. Wipke National Renewable Energy Laboratory, Golden, COMatthew R. CuddyAbstractAn advanced vehicle simulator mod

25、el called ADVISOR hasbeen developed at the National Renewable EnergyLaboratory to allow system-level analysis and trade-offstudies of advanced vehicles. Because of ADVISORs fast execution speed and the open programming environment of MATLAB/Simulink, the simulator is ideally suited for doing paramet

26、ric studies to map out the design space of potential high fuel economy vehicles (3X) consistent with the goals of the Partnership for New Generation of Vehicles (PNGV). Five separate vehicle configurations have been modeled including 3 lightweight vehicles (parallel, series, and conventional drivetr

27、ains) along with 2 vehicles with 1996 vehicle weights (parallel and conventional drivetrains). The sensitivity of each vehicles fuel economy to critical vehicle parameters is then examined and regions of interest for the vehicles mapped out through parametric studies. Using the simulation results fo

28、r these vehicles, the effect of hybridization is isolated and analyzed and the trade-offs between series and parallel designs are illustrated.Advanced Vehicle Simulation Model: ADVISORIn November of 1994, NRELs Center for Transportation Technologies and Systems created a simulation model for advance

29、d vehicles called ADVISOR (ADvanced VehIcle SimulatOR) in the graphical, object-oriented programming language of Simulink/ MATLAB from the MathWorks, Inc. The model was created in support of the hybrid vehiclesubcontracts with the auto industry for the Department of Energy. ADVISOR approximates the

30、continuous behavior of a vehicle as a series of discrete steps during each of which the components are assumed to be at steady state.That is, at each time step, the effects of changing current, voltage, torque, and RPM are neglected. This allows efficiency or power loss tables, which are generated b

31、y testing a drivetrain component at a fixed torque and RPM (and current and voltage, if applicable), to be used to relate the power demands of the components at each time step. A significant advantage of using a model that is in the Simulink/MATLAB environment is the flexibility and ease of changing

32、 the model, such as replacing one control strategy or regenerative braking algorithm with another. MATLAB also allows easy plotting of results that makes detailed analysis of vehicle configurations possible.ADVISOR is driven by the input driving profiles which can be the classic speed vs. time, such

33、 as the federal urban driving schedule (FUDS), or a speed and grade vs. time driving profile. With a given driving profile goal, ADVISOR then works its way backwards from the required vehicle and wheel speeds to the required torques and speeds of each component between the wheels and the energy sour

34、ce, which is either fuel from the hybrid power unit (HPU) or electricity from the batteries. Limits for each of the components are included, so the actual speed vs. time profile computed is the one that is within the limits of all components and includes all component losses and vehicle drag. Figure

35、 1 shows the top level of the series hybrid model in ADVISOR.Figure 1: Top level of ADVISOR series hybrid model Validation of the model and correlation with other vehicle simulations is extremely important to establish the credibility of a model. Through subcontracts with university teams who have b

36、uilt and tested successful hybrid vehicles, NREL has acquired many validated component models that include quantified uncertainties, increasing the credibility of that data. Final vehicle-level validation including detailed uncertainty analysis is scheduled to be completed in September, 1996. In the

37、 meantime, correlation with established public vehicle models has been performed, in addition to some correlations with proprietary models in the automotive industry. Based on these comparisons, ADVISOR appears to be within 2% of most models based on identical inputs. Thus, minimal uncertainty in Ad

38、visors results is introduced by its algorithms; uncertainty in the input data will be the primary source of the uncertainty in Advisors results. Therefore, the source of all input data for the simulations in this analysis is specified below.Vehicles Modeled and AssumptionsFive different vehicle conf

39、igurations were modeled. Both series and parallel hybrids with very low masses and highly Efficient drivetrains were modeled in order to obtain PNGV-like hybrid vehicles that achieved a combined city/highway fuel economy of 80 mpg. These are referred to as 3X vehicles because they get 3 times the fu

40、el economy of a conventional vehicle with a combined city/highway fuel economy of 26.6 mpg (PNGV baseline, PNGV Program Plan). A third configuration was obtained by unhybridizing those vehicles to create a conventional vehicle. The fourth and fifth vehicle configurations were created by taking a con

41、ventional vehicle (at roughly 1.45X due to a diesel engine and manual transmission) and making it a parallel hybrid. Table 1 provides the key differences between the five vehicle configurations modeled and the baseline fuel economy for each vehicle configuration, while Table 2 gives the sources for

42、the input data.Table 1: Key Parameter Values for Vehicle Configurations ModeledVehicleConfig3XParallelHybrid3XSeriesHybrid2.46XLightWt(nonhybrid)1.45XConv.(diesel)1.70XParallelHybridMass(kg)1000.0001000.0001000.0001611.0001611.000BatteryCap.(kWh)1.1003.700n/an/a1.800P e a kH P UPower(kW)31.00030.000

43、47.00077.00052.000Peak MotorPower(kW)12.00041.000n/an/a20.000CDA0.4000.4000.4000.7000.700Crolling-resistance0.0080.0080.0080.0110.011City(mpg)73.80072.30056.10033.80040.700High way(mpg)94.30093.60082.10047.00052.800Combined(mpg)81.80080.50065.40038.70045.300ScalingSince acceleration time from 0-60 m

44、ph and gradeability at 55 mph are performance requirements for all vehicles, the HPU, which in this case is an Audi 5-cylinder turbo diesel engine, and the electric motor have both been sized so that the vehicles meet these performance targets. One major assumption in the scaling of these two compon

45、ents is that the torque/speed power loss maps (equivalent information as in efficiency maps) can be scaled by simply scaling the torque scale on the map. It is known that this is not the most accurate scaling method, but was used for lack of an available and justifiable scaling algorithm.MassThe sou

46、rce of the data for the mass of the conventional 1.45X conventional vehicle and the hybridized version of this vehicle came from the OTA report for a current Ford Taurus. For the 3X vehicles, the mass of 1000 kg is roughly the mass for the Advanced Conventional vehicle for the year 2015 from the OTA

47、 report in which almost all metal components are made of aluminum. This is certainly a significant reduction in mass from todays vehicles; this value was used to allow the efficiencies for other components and parameters to stay within todays technologies or at least the PNGV goals.Hybrid Control St

48、rategiesThe series hybrid uses a simple thermostat on/off strategy to operate the HPU, with the HPU operating at a fixed torque and speed point when it is on. In this study, the HPU turns on when the batteries state-of-charge (SOC) drops below 40% and turns off when the SOC rises above 80%. The para

49、llel hybrid control strategy has the effect of using the batteries for highly transient vehicle launches, unless the batteries are so low that they need to be charged. It can be defined as follows, with high SOC defined as 60% and low SOC defined as 50%:* The HPU does not idle (it turns off when not

50、 needed).* The motor performs regenerative braking regardless of the batteries SOC.* The HPU generally provides the power necessary to meet the trace and the motor generally helps if necessary, with the following exceptions:* when the batteries SOC is low the HPU launches the vehicle and provides ex

51、tra torque to recharge the batteries, and* when the batteries SOC is high, the electric motor only launches the vehicle and no HPU-charging of the batteries occurs.Table 2: Sources of Data for Simulation Inputs and PerformanceRequirementsVehicleParameterValues UsedSource of InputDataCDA0.4,0.7 m2PNG

52、V Goals,Moore, T.CC rolling -resistance0.008,.011OTATransmissionEfficiencies:5 spd。(parallel,conventional) / 1spd. (series)92% /98%AutomotiveEngineering,1996Heat Engine (HPU)Scaled85 kWTDIDieselStock, D., 1990Motor/ControllerScaled75 kWACInductionLesster, L., 1993Energy Storage:BatteriesHorizon12N85

53、Electro source0-60 mph time12.0secondsPNGV GoalsGrade ability at 55mph6.5%indefinitelyMore stringentthan PNGVgoal, which is6.5% for 20MinutesFuel Economy CalculationTo account for changes in the battery packs SOC during a test cycle, a simplified version of the proposed SAE Hybrid Vehicle Test Proce

54、dure is being used. To come up with the city fuel economy, two FUDS are run back-to-back from a high SOC and then from a low SOC, causing a decrease and an increase in battery SOC, respectively. A simple linear interpolation is then used to predict the fuel economy estimate for the vehicle if the ba

55、tteries had no net change in SOC. This ensures a fair comparison between conventional vehicles and hybrid vehicles by accounting for any electrical energy surplus or deficit in the hybrid vehicles battery pack. Without such accounting for the change in SOC of the battery pack, the hybrid might appea

56、r to have an extremely high fuel economy due to electric energy being used in place of fuel energy.Sensitivity of Fuel Economy to Key VehicleParametersA sensitivity analysis of the key parameters for a simulated vehicle illustrates how sensitive the output (fuel economy in this case) is to changes in the input parameters. This allows a side-by-side comparison of the input parameters in order to focus on te