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5、ding J2841 MAR2009 (R) Utility Factor Definitions for Plug-In Hybrid Electric Vehicles Using Travel Survey Data RATIONALE Describing the fuel and electrical energy usage of plug-in hybrid electric vehicles (PHEVs) is very challenging for the reason that these values vary greatly depending upon the d
6、istance traveled between charging. Detailed test procedures recommended for PHEVs are found within SAE J1711, this document serves to supply appropriate UF curves for the equations in that document that weigh the charge depleting mode operation with the charge-sustaining operation depending upon the
7、 charge-depleting mode distance measured from the test. The original issue of SAE J2841 introduced the Utility Factor based upon a mileage-based “Fleet” analysis of the US DOT National Household Transportation Survey data. This issue of SAE J2841 adds more options to the Utility Factor calculations
8、by analyzing another data set from a Georgia Tech called the “Commute Atlanta” in order to find a vehicle-weighted analysis. 1. SCOPE This SAE Information Report establishes a set of “Utility Factor” (UF) curves and the method for generating these curves. The UF is used when combining test results f
9、rom battery charge-depleting and charge-sustaining modes of a Plug-in Hybrid Electric Vehicle (PHEV). Although any transportation survey data set can be used, this document will define the included UF curves by using the 2001 United States Department of Transportation (DOT) “National Household Trave
10、l Survey” and a supplementary dataset. 1.1 Purpose In use, the fuel and energy consumption rates of a PHEV vary depending upon the distance driven between charge events. For PHEVs, the baseline assumption regarding any UF is that operation starts fully charged and begins in charge-depleting mode. Ev
11、entually, the vehicle must change to a charge-sustaining mode. The vehicle miles traveled between charge events determines how much of the driving is performed in each of the two fundamental modes. A second assumption is that charging occurs every day after the days driving is complete, i.e. once pe
12、r day. In the absence of PHEV driver behavior data, the two additional unknown driver behavior issues of (1) how often charging occurs during the day (“opportunity charging”) and (2) how often a driver will forget to charge, are assumed to be equally offsetting, thus the baseline assumes one charge
13、per day of operation. Given the previous assumptions, a UF describes the fraction of driving in each of the fundamental modes using a given set of recorded in-use driving data. Driving statistics from the 2001 National Household Travel Survey and a supplementary dataset are used as inputs to the UF
14、creation to provide UF curves applicable to a vehicles charge-depleting mode results. Copyright SAE International Provided by IHS under license with SAENot for ResaleNo reproduction or networking permitted without license from IHS-,-,-SAE J2841 Revised SEP2010 Page 2 of 452. REFERENCES 2.1 Applicabl
15、e Documents SAE 810265, Burke, A.F. and Smith, G.E., “Impacts of Use-Pattern on the Design of Electric and Hybrid Vehicles” SAE J1711, “Recommended Practice for Measuring the Exhaust Emissions and Fuel Economy of Hybrid-Electric Vehicles, Including Plug-in Hybrid Vehicles” SAE J1715, “Hybrid Electri
16、c Vehicle (HEV) the total distance traveled is greater than zero. For an SUF, if a vehicle has no weighted distance, that vehicle should be removed from the data set for that cycle-specific calculation. For these SUFs, if the weighted distance traveled is zero, the SUF is undefined. One particular o
17、ption for creating SUF curves may be computed from travel surveys, such as the NHTS, using only the trip distances and trip travel times. Since only trip information is available, each trip is wholly assigned to either city or highway driving. In the NHTS, only average trip time and trip distance is
18、 available and thus an allocation must be based on calculated vehicle speed alone. For example, consider the common assumption of 55% of vehicle miles traveled (VMT) in urban settings and 45% in highway settings for partitioning trips into bins for city and highway driving. The slowest 55% of VMT is
19、 assigned to city driving. The trips that are associated with this travel are considered to be city driving samples. The remaining trips are assigned to highway driving. Figure 10 shows the cumulative VMT from the NHTS versus the average trip speed. This chart is generated by taking each trip, calcu
20、lating the average speed for the trip, then sorting the list and plotting the cumulative distance versus the average trip speed. The speed below which 55% of VMT occurs is 42 mph. For illustration purposes, an additional set of SUF curves have also been included in the final coefficients table with
21、a 43% city versus 57% highway split. More discussion regarding the two different split proportions may be found in the Federal Register, Vol. 71 No.248. FIGURE 10 - CUMULATIVE VMT VERSUS AVERAGE TRIP SPEED Copyright SAE International Provided by IHS under license with SAENot for ResaleNo reproductio
22、n or networking permitted without license from IHS-,-,-SAE J2841 Revised SEP2010 Page 12 of 45To calculate the urban weight for a days travel, nkd,use =nknkMmmnkMmthresholdmnkmnknkurbantsstw,1,1,0else,if(Eq. 9) To calculate the highway weight for a days travel, nkd,use=nknkMmmnkMmthresholdmnkmnknkhi
23、ghwaytsstw,1,1,0else,if(Eq. 10) In either equation, if no travel occurs (e.g.=Nnmnkt1,equals zero), the weight is assigned a value of zero. In both of these equations, the variables in Tables 3 and 4 are used. TABLE 4 - CYCLE-SPECIFIC UF EQUATION VARIABLES Variable Definition nkM,Number trips taken
24、by vehicle k on day nmnks,7.2 Average trip speed for trip m by vehicle k on day nthesholds7.2.1.1.1.1 Trip speed threshold between urban and highway mnkt,7.2.1.2 Distance driven on trip m by vehicle k on day nUsing this process with the 2001 NHTS data and city/highway splits of 55/45 and 43/57 perce
25、nt city to percent highway driving, the City and Highway Specific FUF curves are shown in Figure 11. As a supplement to Equations 7-10, Appendix C provides an example Python script file which can be used to parse the NHTS data and create the City and Highway Specific FUF curves discussed in this doc
26、ument. Copyright SAE International Provided by IHS under license with SAENot for ResaleNo reproduction or networking permitted without license from IHS-,-,-SAE J2841 Revised SEP2010 Page 13 of 45FIGURE 11 - CITY AND HIGHWAY SPECIFIC FUF VALUES The corresponding fit coefficient table for these curves
27、 is shown in Table 5. Appendix E contains tables of the CSFUF and HSFUF equations evaluated at 1 mile increments and rounded to the nearest 0.001. For non-integer distances, it is recommended that the appropriate UF fit equation be evaluated to calculate the UF. TABLE 5 - FIT COEFFICIENTS FOR EXAMPL
28、E CITY/HWY SPECIFIC FUF CURVES Value 55/45 Split - City 55/45 Split - Hwy 43/57 Split - City 43/57 Split - Hwy norm_dist 399 399 399 399 C1 1.486E+01 4.8E+00 1.69E+01 5.43E+00 C2 2.965E+00 1.3E+01 1.84E+00 1.49E+01C3 -8.405E+01 -6.5E+01 -9.63E+01 -8.00E+01 C4 1.537E+02 1.2E+02 1.86E+02 1.50E+02C5 -4
29、.359E+01 -1.0E+02 -5.60E+01 -1.26E+02 C6 -9.694E+01 3.1E+01 -1.23E+02 3.95E+01C7 1.447E+01 - 1.99E+01 - C8 9.170E+01 - 1.21E+02C9 -4.636E+01 - -6.30E+01 - Max Error 0.00558 0.00387 0.00683 0.00487 7.3 Discussion Regarding the Use and Creation of Specific Utility Factors There are numerous ways to ge
30、nerate the weighting for each data point, dkn, and therefore many possible methods and SUF curves. Moreover, the applicability of the allocation between types of driving is only as good as the weighting method used to parse the cycles. If continuous data for driving is available, features of each dr
31、iving trace can be used to determine the weighting applied to a particular data point. For example, to build a City Specific UF, the weighting for each data point could be determined from features such as number of stops, top speed, average speed, and average moving speed. Although the weighting cri
32、teria may change, the process in Equations 7 to 10 should remain the same with the exception of the threshold in Equations 9 and 10. Given the many options for creating SUF curve types, care must be taken to ensure that sufficient data exists to create a representative SUF curve and that the SUF use
33、d is appropriate for the given analysis situation. Copyright SAE International Provided by IHS under license with SAENot for ResaleNo reproduction or networking permitted without license from IHS-,-,-SAE J2841 Revised SEP2010 Page 14 of 45To illustrate the breadth of options regarding methods to all
34、ocate City and Highway driving, several alternative options are presented here for discussion. The first option is similar to the previous cutoff speed method, but in this case two distinct speeds are used to differentiate between City and Highway driving style. In addition to the use of two cutoff
35、speeds, this method assigns a City weighting between 0 and 1 according to average trip speed whereas the previous method fully allocated a trip to either City or Highway driving. For trips with an average speed above 60 mph, the trip is assumed to 100 percent Highway driving and thus receives a valu
36、e of 1 for the Highway weighting and a value of 0 for the City weighting. Similarly, trips with an average speed under 25 mph are wholly assigned to City driving. Between these two cutoff speeds, trips receive a weighting that is scaled linearly between 0 and 1. In order to retain a City/Highway dri
37、ving mix similar to the previous method, the City cutoff speed (25 mph) for this example was chosen to create a 55 percent City to 45 percent Highway mix relative to total miles driven. The Highway cutoff speed (60 mph) was simply chosen as a reasonable estimate. Figure 12 illustrates the weighting
38、values as a function of average trip speed. FIGURE 12 - CITY AND HIGHWAY WEIGHTS FOR ALTERNATIVE SPEED-BASED ALLOCATION METHOD Another related method for allocating trips based on average speed is to simply create a cutoff speed for exclusively City driving and exclusively Highway. Trips between the
39、se two cutoff speeds are assumed to be equally likely to be City of Highway driving and thus are included in both the City and Highway datasets used for calculating the SUFs. This method creates two sets of overlapping data to create the City and Highway SUFs while removing the trips that are unlike
40、ly to be driven in the style of the SUF being calculated. Figure 13 illustrates the three distinct sections; the cutoff speeds shown are used for example only and would need to be adjusted depending on the desired amount of overlap between City and Highway driving. In this example, the actual mix of
41、 City and Highway relative to VMT is unknown due to the overlapping region. FIGURE 13 - DRIVING STYLE SECTIONS FOR OVERLAPPING DATASET ALLOCATION METHOD Copyright SAE International Provided by IHS under license with SAENot for ResaleNo reproduction or networking permitted without license from IHS-,-
42、,-SAE J2841 Revised SEP2010 Page 15 of 45The last alternative method proposed for discussion attempts to incorporate additional information into the determination of driving style. This method seeks to categorize an entire day of driving data using the average daily speed and the daily amount of tri
43、ps. It is assumed that in addition to reduced speed, City driving will contain more trips compared to Highway driving. This method allocates the entire day to City or Highway driving due to the nature of the NHTS data, but a similar method could be done on a trip-by-trip basis using stops as opposed
44、 to trips. Figure 14 illustrates the allocation of City and Highway trips using a speed/number-of-trips dividing line. The dividing line allows for higher speed driving to still be categorized as City driving if there are a sufficient number of trips during the day. The dividing line was set so that
45、 the driving mix was again 55 percent City to 45 percent Highway. FIGURE 14 - EXAMPLE ALLOCATION OF TRIPS USING AVERAGE SPEED AND NUMBER OF TRIPS Additionally, not all datasets should be parsed using a prescribed percentage mix of City and Highway driving. For example, if a particular dataset is kno
46、wn to be biased towards a particular driving style in terms of total vehicles sampled, using a percentage of miles driven weighting criterion may not be the most representative methodology. Moreover, if a method to allocate driving style is known to work well, this allocation technique should take p
47、recedence over the expected percentage of VMT methodology discussed previously. In the case of the 2001 NHTS dataset, a national sampling, it is assumed that the mix of driving should be reasonably similar to the existing City versus Highway trends. As with the discussion in the previous paragraphs,
48、 more data would be needed to more conclusively allocate between City and Highway driving for the included NHTS sample or any other dataset used to create a Specific Utility Factor. 8. NOTES 8.1 Marginal Indicia A change bar (l) located in the left margin is for the convenience of the user in locating areas where technical revisions, not editorial changes, have been made to the previous issue of this document. An (R) symbol to the left of the document title indicates a complete revision of the document, including technical revisions. Change
