Analysis of Fiat Ecodrive data

Analysis of Fiat Ecodrive data
WLTP-DHC-xxx Analysis of Fiat Ecodrive data By H. Steven Updated version 1

Introduction Within the discussion about weighting factors for the 4 phases of the WLTC the author was asked to analyse in-use driving behavior data provided by FIAT. The following text is copied from a FIAT presentation: Fiat Group Automobiles in November 2008 introduced a eco-technology called eco:Drive™ in vehicles to encourage environmentally aware driver behaviour. Fiat eco:Drive™ allows customers to collect driving data from their vehicles, These data, through a personal computer application, are analyzed by specific algorithms in order to obtain personalized feedback on how to change driving style to achieve maximum fuel efficiency from the vehicle. 2

Database and preprocessing
The data was delivered as text files ( in total). The first line contained information about the vehicle and the driver. Information about the country could be obtained from a cross reference list. Table 1 gives an overview of vehicles, countries and number of drivers. The data was imported in a series of ACCESS databases. Each text file was interpreted as journey or cycle and got a cycle number as identifier. Each user got a driver ID. The cycle durations varied from 21 s to 4,8 h (average 12,2 min). 3

Database Table 1 4

Preprocessing On this data the same preprocessing was applied as for the WLTP in-use database. The vehicle speed trace was smoothed by a Hanning filter, The data was separated into short trips and stop periods. The acceleration a was calculated based on the smoothed speed signal. Short trips were indicated as not ok, if a > 4 m/s² or a < -4,5 m/s², v_start and or v_end > 12,6 km/h (incomplete ST), if the absolute difference between original speed and smoothed speed is > 7 km/h (jumps in the speed trace) 5

Preprocessing Within a cycle the short trips and the stops were numbered in ascending order. The stops were linked to the short trips by these numbers. Stops were indicated as not ok, if The allocated short trips were not ok, The duration exceeded 600 s (1365 of 1,95 mio stops). Figure 1 shows the speed trace of an erroneous cycle period. Such errors increase the stop time. Erroneous cycles were excluded from the further analysis. This reduced the total mileage from 2,44 million km to 2,33 million km (-4,5%). 6

Erroneous vehicle speed trace
Figure 1 7

Further check of the results
The remaining dataset consists of 3332 different vehicle/driver combinations. Since vehicle speeds and stop percentages are highly influenced by the area of the abode of the driver, a comparison of the results should be based on driver specific analyses. This requires a statistically significant amount of data in terms of monitoring days and mileage. Figure 2 shows, that 362 vehicle/driver combinations (11%) had just one monitoring day, 1775 vehicle/driver combinations (53%) had less than 14 monitoring days. Since these combinations represent only 13,5% of the total mileage, they were disregarded for the further analysis. 8

Frequency distribution of monitoring days
Figure 2 9

Vehicle/driver specific results
In a first analysis step for the remaining vehicle/driver combinations mileage, drive time, stop time, average speeds and stop percentages were calculated per monitoring day. The same analysis was done for the EU WLTP in-use database. The results for all vehicle/driver combinations and all monitoring days are shown in figure 3. The correspon-ding values for the WLTC 5.3 are shown for comparison. Both datasets cover the same area and have similar trend lines. As one would expect, the WLTC 5.3 average is close to the average of the EU WLTP in-use database. 10

p_stop vs v_ave Figure 3 11

The Fiat ecodrive dataset has a lower average speed (-15% compared to the WLTC 5.3) and a higher stop percentage (+15% compared to the WLTC 5.3). Both datasets show large variances not only in average speeds and stop percentages but also in the average daily distances (see figure 4). And there is a certain correlation between the average speed and the average distance per day. The 142 vehicle/driver combinations of the WLTP DB show a significant higher average daily distance than the Fiat ecodrive DB. This might at least partly be explained by differences in the vehicle sample (see table 2). 12

v_ave vs d_ave per day Figure 4 13

Vehicle samples Table 2 14

Figure 5 shows the average values for each vehicle/driver combination of the two datasets. But there are large day to day variations for a given vehicle/driver combination. For the Fiat ecodrive DB the ratios between the standard deviation of v_ave and the average range from 6% to 87% (average 31%), the corresponding values for the stop percentages range from 13% to 135% (average 49%). Similar ranges were found for the WLTP DB. Figure 6 shows examples for day to day variations. Figure 7 shows the time history of the most extreme day (v_ave = 13,9 km/h, p_stop = 64,5%). 15

Vehicle speed vs time Figure 7 18

But low average speeds and high stop percentages could also be caused by the application of the “not ok” filters. One example for that is one vehicle from the EU WLTP database with v_ave = 16,4 km/h and p_stop = 40,3% (see figure 5). It belongs to vehicle 3 of the Spanish subsample. Figure 8 shows the time history of one day for this vehicle. Major parts of the speed pattern, especially for short trips with high maximum speeds are indicated as not ok, because the accelerations are unrealistic high (up to 8,4 m/s²). 19

Vehicle speed vs time Figure 8 20

The deletion of these short trips drops the average speed and distance and increases the stop percentage drastically. For this particular vehicle this effect did also influence the measurement results for other monitoring days and thus the overall averages (see figure 9). In order to get a more general overview about the influence of the application of the “not ok” filters, the results with and without the filter were compared for each vehicle/driver combination for both databases (WLTP and ecodrive). The results are shown in figure 10, where the relative change in v_ave is plotted versus the ratio of the distance for “ok” short trips and the total distance. 21

v_ave versus p_stop Figure 9 22

Delta_v_ave vs p_dist_not_ok
Figure 10 23

The highest influence was found for the ecodrive database without any requirements for the monitoring period (light blue squares, all 3332 vehicle/driver combinations). For a significant number of cases the “not ok” percentage on the driven distance is more than 30%, which reduces the average speed significantly. The effect is less pronounced for the WLTP DB and is significantly reduced for the ecodrive DB by the requirement that at least 14 monitoring days are requested. This requirement limits the “not ok” percentage on the driven distance to 30% and thus justifies its application. 24

The effect on the average speed was 0,4 km/h for the WLTP DB, 0,7 km/h for the complete ecodrive DB and 0,2 km/h for the ecodrive DB with the 14 days monitoring requirement. But the average speeds in the further analysis were not corrected according to this effect. 25

Country specific results
In both databases no systematic differences were found between the vehicle models. But the results for the different countries showed sometimes quite significant differences. France has the highest mileage share in the WLTP DB. The mileage of the two WLTP datasets from France sum up to km. Almost the same mileage ( km) in the Fiat ecodrive DB is related to France. The results for the different vehicle/driver combinations are compared in figure 11. The two WLTP datasets have a slightly higher average speed but lower stop percentage than the WLTC 5.3. 26

France, p_stop vs v_ave Figure 11 27

The Fiat ecodrive dataset has a lower average speed but almost the same stop percentage compared to the WLTC 5.3. If one merges all datasets, the difference of the average to the WLTC 5.3 is quite low (-6% for v_ave as well as for p_stop). It must be mentioned that each vehicle/driver combination got the same weight for the calculation of the averages. The second largest dataset in the WLTP DB is from Belgium (about km). The dataset from Belgium in the Fiat ecodrive DB is rather small (only 6000 km). 28

Figure 12 shows a comparison of the results. Even the average of all datasets has a higher average speed and a lower stop percentage than the WLTC 5.3. Next in the WLTP DB with respect to mileage is Italy (57000 km). As one would expect, the by far highest contribution in the Fiat ecodrive DB is from Italy ( km). Figure 13 shows a comparison of the results. Since the data amount in the Fiat ecodrive DB is so high, the results are shown for each vehicle model separately. The WLTP DB dataset from Italy has by far the highest average speed value and the lowest stop percentage (57,8 km/h, 11,3%). 29

Belgium, p_stop vs v_ave
Figure 12 30

Italy, p_stop vs v_ave Figure 13a 31

Italy, p_stop vs v_ave, averages
Figure 13b 32

The average speed is much higher and the stop percentage lower than the WLTC 5.3. The Fiat ecodrive data has lower average speeds and higher stop percentages than the WLTC 5.3 but with significant differences between the vehicle models. The average for vehicle 6 is pretty close to the WLTC 5.3, the average for vehicle 2 is still reasonably close to the WLTC 5.3, but the averages for the other vehicle models are between 17% and 25% lower in speed and 19% to 31% higher in stop percentage. On the other hand, the km mileage of the WLTP DB cannot be ignored and the average for the mid size and compact cars in the Fiat ecodrive DB are also pretty close to the WLTC 5.3 (v_ave -6%, p_stop +7%). 33

Further investigations are necessary in order to assess the results for Italy. Statistical data about the fleet composition in terms of vehicle subcategories would be helpful. Figure 14 shows the results for the WLTP DB from Slovenia (15 vehicles, km) and Sweden (6 vehi-cles, km). The average speeds fit well with the WLTC 5.3, the average stop percentage for Sweden is even significantly lower. The in-use data collection for Sweden was carried out in Gothenburg and the surrounding area (Västra Götaland). 34

Slovenia, Sweden, p_stop vs v_ave
Figure 14 35

The Fiat ecodrive DB does not contain any data from Sweden but some data from Denmark (23 vehicle/driver combinations, km). The results are also shown in figure 11. The average values are close to the WLTC 5.3 values. For Germany the in-use data sample in the WLTP DB is rather small (8 vehicles, km); in the Fiat ecodrive DB Germany has a much higher share (205 different vehicle/driver combinations, km). The p_stop / v_ave values for each vehicle/driver combination are shown in figure 15. Because of the high amount of ecodrive data, this data is separated by different symbols for the vehicle models. 36

Germany, p_stop vs v_ave
Figure 15a 37

Germany, p_stop vs v_ave, averages
Figure 15b 38

The vehicle model specific differences in the ecodrive data are much higher than in the Italian sample. The average values vary between v_ave = 39,8 km/h and p_stop = 14,2% for vehicle 7 and v_ave = 54,7 km/h and p_stop = 7,1% for vehicle 2. The averages for vehicle 6 are almost identical to the WLTC 5.3, the averages of the WLTP DB and for vehicle 3 are close to those of WLTC 5.3. Vehicle 5 has a slightly higher average speed than the WLTC 5.3 but a significantly lower stop percentage. One can conclude that the WLTC 5.3 represents the German in-use data reasonably good. 39

An even higher mileage than for Germany in the Fiat ecodrive DB is related to the UK (221 vehicle/driver combinations, km). But the mileage shares between the vehicle models vary a lot. 57% of the mileage belongs to the mini car with Petrol engine, 15% to the mini with Diesel engine. Another 22% belongs to the subcompact car with Petrol engine. The shares of the two compact cars sum up to almost 6% only. The WLTP in-use data sample is in the order of the two compact cars from the Fiat ecodrive DB (10 M1 vehicles, km). The results are shown in figure 16. 40

UK, p_stop vs v_ave Figure 16a 41

UK, p_stop vs v_ave, averages
Figure 16b 42

Concerning the ecodrive data there are significant differences between the three models with Petrol engine and the two models with Diesel engine. The latter have higher average speeds and lower stop percentages, but the average speeds are still slightly below WLTC 5.3. The WLTP DB average speed is almost the same as for the two ecodrive models with Diesel engine, but the stop percentage is significantly lower (only 10,1%). The ecodrive data as a whole indicates lower average speeds (-17%) and higher stop percentages (+15%, 15,5% instead of 13,4%) but the mileage shares of the sample is certainly not in line with the stock statistics. 43

The remaining countries in the WLTP in-use DB are Poland and Spain. The WLTP DB contains data of 9 different vehicles from Poland. The measurements were performed in Krakow and the surrounding area. The total mileage is km. The Fiat ecodrive DB contains data for Poland for 63 vehicle/driver combinations (6 mid size, 13 compact, 4 mini and 40 subcompact cars, km total mileage. The results are shown in figure 17. Average speeds and stop percentages are in total significantly lower/higher than the WLTC 5.3 and the data from the other countries. 44

Poland, p_stop vs v_ave Figure 17 45

The WLTP DB represents the lowest v_ave (below 30 km/h) and highest p_stop combination. The Diesel engine variant of the two compact car models (vehicles 3 and 6) has a 19% higher average speed and a 10% lower stop percentage than the Petrol engine variant. The Fiat ecodrive DB contains data of 32 different vehicle/driver combinations with a total mileage of km for Spain. More than 50% of the mileage belongs to the two mini car models, another 34% belongs to the compact car with Petrol engine, 9% belongs to the mid size car model and the rest to just one vehicle/driver combination of a subcompact model. 46

The WLTP DB contains data for only 4 vehicles from Spain with sufficient monitoring days (total mileage 8400 km). The results are compared in figure 18. The average speeds are similar to the Polish results (in total 16% lower than WLTC 5.3), the stop percentages are even higher than for the Polish data (in total 32% higher than WLTC 5.3). The significant differences between the Petrol and Diesel engine variants of the mini car models are noticeable (38% higher average speed and 19% lower stop percentage for the Diesel variant compared to the Petrol variant). 47

Spain, p_stop vs v_ave Figure 15 48

The averages for the Diesel variant are not so far away from WLTC 5.3. The Fiat ecodrive DB contains also data from Portugal, the second country on the Iberian peninsula. This data consists of 35 vehicle/driver combinations with a total mileage of km. Almost 50% of the mileage belongs to the two mini cars and another 50% to the compact car with Diesel engine. The variation range between drivers and vehicles is significantly lower than for Spain, the average speeds are slightly higher the stop percentages significantly lower than for Spain (see figure 19). 49

Portugal, p_stop vs v_ave
Figure 19 50

And there are no significant differences in the average values between the two mini car variants (Petrol/Diesel), although the variation range for the Diesel variants is higher than for the Petrol variants. Compared to the WLTC 5.3 the average speed of the whole sample is 14% lower, the stop percentage 12% higher. In addition to that the Fiat database contains also data for Austria ( km), Greece (7 400 km, Netherland ( km) and Switzerland ( km). Since the mileage for Greece is rather low, the results will not be shown in this presentation. 51

The data for Austria consists of 30 different vehicle/driver combinations. The results are shown in figure 20. The averages are in good accordance with the WLTC 5.3. The data from the Netherland covers 16, the data from Switzerland 15 vehicle/driver combinations. The results are shown in figure 21. The average speeds are close to the WLTC 5.3, the same accounts for the average stop percentage for the Netherland, the average stop percentage for Switzerland is only 9.2%. 52

Austria, p_stop vs v_ave
Figure 20 53

NL and CH, p_stop vs v_ave
Figure 21 54

Comparison of the results for countries and vehicle classes
The average values for v_ave and p_stop are summarized for the Fiat ecodrive data in tables 3a and 3b per vehicle model and country. A comparison of v_ave between the ecodrive data and the EU WLTP data is shown in table 4 per country and in table 5 per vehicle class. The data does not show any systematic influence of vehicle model or country. 55

v_ave per country and vehicle
Table 3a 56

p_stop per country and vehicle
Table 3b 57

v_ave per country Table 4 58

v_ave per vehicle class
Table 5 59

Additional analysis results
Since it was criticized that the analysis so far is only based on average speeds and stop percentages, analysis results about Short trip distance distributions, Vehicle speed distributions and Acceleration distributions are added to this updated version. 60

Short trip distance distributions
Figure 22 shows the short trip distance distributions of the EU WLTP DB and the Fiat ecodrive DB. Up to a cumulative frequency of 75% both distributions are almost identical. 75% is equivalent to short trips up to 1200 m. Above this threshold the Fiat ecodrive DB contains lower shares for high distances. The 90% values are 3750 m for the Fiat ecodrive DB and 4300 m for the WLTP DB. The corresponding values for a cumulative frequency of 95% are 7600 m and 9800 m. The average short trip distances are 1748 m for the Fiat ecodrive DB and 2364 m for the EU WLTP DB. 61

Figure 22 62

But there are significant differences in the short trip distance distributions between the datasets of the different member states, in fact in both databases (see figures 23 and 24). The distributions for the different member states in the Fiat ecodrive DB are within the bandwidth of the variations in the EU WLTP DB (see figure 24). And the differences between the 7 vehicle models in the Fiat ecodrive DB are much smaller than the differences between the member states for all vehicles (see figures 24 and 25). 63

Figure 23 64

Figure 24 65

Figure 25 66

Vehicle speed distributions
Figure 26 shows the overall vehicle speed distributions of the EU WLTP DB and the Fiat ecodrive DB for vehicle speeds above 2,5 km/h. As could be expected from the average speed analysis, the Fiat ecodrive distribution has higher shares for lower speeds. But once again, there are significant differences between the datasets from the different member states in both databases (see figures 27 and 28) and these differences are higher than the differences between the 7 vehicle models in the Fiat ecodrive DB (see figure 28 and 29). 67

Figure 26 68

Figure 27 69

Figure 28 70

Figure 29 71

Two things need to be mentioned in particular: The vehicle speed distribution for NL in the Fiat ecodrive DB is closest to the EU WLTP overall distribution and shows significant higher shares for high speeds than the distribution for DE (see figure 28). It is most probable, that this difference is not in line with the “true” distributions for both countries. Secondly, the speed distribution for the Fiat Bravo with Diesel engine fits pretty good to the WLTP overall distribution. This vehicle model represents a bit more than km mileage in the Fiat ecodrive DB. 72

Acceleration distributions
Figure 30 shows the acceleration distributions for the Fiat ecodrive DB and the EU WLTP DB. At the first glance one could conclude that there is no difference at all. But this is due to the fact that the acceleration values decrease with increasing vehicle speed and that the vehicle speeds of the EU WLTP DB are somewhat higher than the speeds of the Fiat ecodrive overall DB. When one compares the corresponding vehicle speed distributions for specific vehicle speed classes, one can see that the accelerations in the Fiat ecodrive DB are a bit lower than in the EU WLTP DB (see figure 31). 73

Figure 30 74

Figure 31 75

Concerning the differences between different member states and different vehicle models in the Fiat ecodrive DB the following should be mentioned. At 30 km/h the differences between the member states are higher than the differences between the 7 vehicle models (33% versus 22% for the 95% percentile, see figures 32 and 33). At 60 km/h the rank order is reversed, the differences between the member states are smaller than the differences between the 7 vehicle models (20% versus 40% for the 95% percentile, see figures 34 and 35). 76

Figure 32 77

Figure 33 78

Figure 34 79

Figure 35 80

This result could be explained by the hypothesis that the acceleration behaviour at low speeds is more influenced by the traffic conditions than by the acceleration potential of the vehicles, while at high speeds it is more influenced by the acceleration potential of the vehicle than by the traffic conditions. But it needs to be investigated further, whether this hypothesis is well founded. 81

Summary and conclusions
Within the discussion about weighting factors for the 4 phases of the WLTC the author was asked to analyse in-use driving behavior data provided by Fiat. The data was delivered as text files ( in total). The first line contained information about the vehicle and the driver. Information about the country could be obtained from a cross reference list. The data covers 7 Fiat car models, 1 mid size, 2 compact, 2 subcompact and 2 mini cars. The pairs represent the same model, once with a Petrol and once with a Diesel engine. On this data the same preprocessing was applied as for the WLTP in-use database. 82

Erroneous cycles were excluded from the further analysis. This reduced the total mileage from 2,44 million km to 2,33 million km (-4,5%). The remaining dataset consists of 3332 different vehicle/driver combinations. Since the analysis required a statistically significant amount of data in terms of monitoring days and mileage, vehicle/driver combinations with less than 14 monitoring days were excluded. This reduced the mileage to 1,9 million km and the number of vehicle/driver combinations to Table 6 shows the mileage distribution on countries and vehicle models. 83

Mileage per country and vehicle
Table 6 84

The EU WLTP in-use data was recalculated in the same way for comparison reasons. The results in terms of average speed and stop percentage for a vehicle/driver combination (and a monitoring period of at least 14 days) can vary between 15 km/h and 40% and 77 km/h and 3%. No systematic influence of vehicle class or country was found. It can rather be concluded that the area (agglomeration or rural) and the individual usage of the car (driver demand) are the most influencing factors. Both factors cannot be scaled for the ecodrive dataset as well as for the EU WLTP dataset. 85

It can further be assumed that the ecodrive dataset, which is dominated by the two mini cars and a subcompact car, is not in line with statistics about the mileage distribution on vehicle classes. This mileage distribution may vary between different countries. And it can also be assumed that the mini cars quite often are used as second car within a household. This assumption is supported by the fact that the average daily distances in the ecodrive dataset is significantly lower than for the EU WLTP dataset. Since there is a certain correlation between the average speed and the daily driven distance, the difference in average speed can partly be explained by this difference. 86

Without any weighting the average speed of the ecodrive dataset is 39,8 km/h and the stop percentage is 15,1%. This means that the average speed is 14,4% lower than the average speed of WLTC 5.3 and the stop percentage is 1,7% higher than the stop percentage of the WLTC 5.3 (in absolute values). If one would apply a mileage weighting for the different countries within the ecodrive DB based on the TREMOVE data used within the WLTP development process, the average speed for the ecodrive data would be 40,4 km/h and the stop percentage 14,8%. This is 13% lower than the average speed of WLTC 5.3 and the stop percentage is 1,4% higher than the stop percentage of the WLTC 5.3 (in absolute values). 87

If one would merge the ecodrive dataset and the EU WLTP dataset and restrict the averaging process to those countries that are represented in the WLTP database and use the same weighting factors which were used within the WLTP development process, the average speed would be 42,3 km/h and the stop percentage 13,9%. This is 9% lower than the average speed of WLTC 5.3 and the stop percentage is 0,5% higher than the stop percentage of the WLTC 5.3 (in absolute values). These results do not justify immediate modifications of the WLTC and do not support the application of the weighting factors proposed by France. 88

But the analysis showed also quite clearly, that it is difficult to determine the “true” value of average speed for Europe without additional statistical data. Further investigations are necessary in order to verify the results. Statistical data about the fleet composition in terms of vehicle subcategories and annual mileage would be helpful. But with respect to the stop percentage it can clearly be stated based on this analysis, that the stop percentage of the WLTC suits well to its average speed. 89

With respect to the short trip distance distributions it can be concluded that there is no difference in the traveled distances for 75% of the total driving time. This corresponds to short trips up to 1200 m. For higher short trip distances the Fiat data shows higher shares for lower distances compared to the EU WLTP DB. It cannot be assessed whether this difference is caused by the limited vehicle sample of the Fiat DB compared to the broader sample of the EU WLTP DB. The vehicle speed distributions confirm what was already concluded from the average speed analysis. 90

At the first glance one could conclude from the acceleration distribution analysis that there is no difference at all between the Fiat ecodrive DB and the EU WLTP DB. But this is due to the fact that the acceleration values decrease with increasing vehicle speed and that the vehicle speeds of the EU WLTP DB are somewhat higher than the speeds of the Fiat ecodrive overall DB. When one compares the corresponding vehicle speed distributions for specific vehicle speed classes, one can see that the accelerations in the Fiat ecodrive DB are a bit lower than in the EU WLTP DB. 91

Final remarks The WLTC is intended to be used for the determination of pollutant exhaust emissions and CO2 emissions in a couple of years from now. That means it should be orientated more on future trends rather than on the past or the current situation. Jourmard estimated in [1] almost 10 years ago already the following trend in the mileage distribution: Slight decrease of the urban part from 35% in 1970 to 33% in 2020, significant decrease of the rural part (from 62% to 39%) and a significant increase of the motorway part (from 3% to 28%). The percentages estimated for 2020 reflect already quite well the current situation for Germany. 92

Final remarks This trend would undoubtedly lead to an increase of the average speeds. Another argument can be deducted from the efforts to reduce pollutant emissions at hotspots in agglomera-tions. The by far most effective measure – apart from further reductions by improvements of the aftertreatment systems at the vehicle – is the reduction of time shares of stop & go conditions in the traffic condition mix. Several research projects are aiming at the development of measures to achieve this goal. Also this would result in an increase of the average speeds. 93

Literature [1] INRETS report LTE 0420, Transport routier - Parc, usage et émissions des véhicules en France de 1970 à 2025, Jourmard, September 2004, 94

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