5. Introducci´ on a los contrastes de hip´ otesis 79
5.5. Contrastes para un porcentaje
The continuous on-road emissions and operating data collected by the Semtech-DS and PHEV Sprinter’s data-logging module (DLM) were categorized and aggregated for the sections of roadway being considered. By compiling the continuous, second-by-second data into calculated averages for specific roadway links, any autocorrelation and lack of normality apparent in the continuous dataset was negated, and parametric statistical tests deemed acceptable.
Analysis of variance (ANOVA) conducted on the data (compiled from both 10th and 11th street links) shows that auxiliary system use is a statistically significant factor (α<0.05) for the diesel internal combustion engine (dICE) power output, normalized for distance traveled, W/kgkm, (p=0.044), with air conditioning employment resulting in higher power demands from the ICE. The same analysis on the electric motor (EM) did not yield
statistically significant differences between EM power output according to auxiliary system use (air conditioning, heater, or no system). However, since the PHEV Sprinter was
operating in charge-sustaining mode (hybrid operation), it is likely that the dICE was preferentially employed to handle overages in power demand due to AC use.
Table 5.1: Power output according to auxiliary system use for compiled dataset of both
While originally expected to be similar, significant differences appeared between the power output (on a per kilometer basis) required to travel 10th street versus 11th street, with 11th street requiring an average of 731.1W/kgkm total power output while 10th street only required an average of 402.6W/kgkm. Power output proved to be a statistically significant response to traveled street when the analysis of ICE and EM power output were performed separately (p=0.000, p=0.018, and p=0.007 for Total Power, EM Power, and ICE Power, respectively). Despite having similarly distributed traffic schemes and identically imposed speed limits, ANOVA analysis shows a statistically significant difference between the average velocity traveled along 10th and 11th streets (p=0.045), with 10th street travel
resulting in an average velocities more than 15% greater than 11th street (mean velocities of 35.5km/h and 30.3km/h for 10th and 11th street links, respectively). Some of the components of the PHEV’s exhaust emissions also demonstrated a statistically significant response to
roadway link with carbon dioxide, carbon monoxide, and total hydrocarbon emissions varying between the 10th street and 11th street links (Table 5.2).
Similar to power output and velocity, some of the measured emissions did show a statistically significant (<0.05) response to link traveled. Based on ANOVA analysis, carbon dioxide, carbon monoxide, and total hydrocarbon emissions are significant responses to the specific link being traveled (10th Street versus 11th Street). Despite roadway similarity as determined by link characteristics (speed limit, topography, traffic signals) and operator observation (traffic density and behavior), substantial differences emerged between the links traveled. It is likely that differences in link topography and/or the impact of vehicle
operation according to the point in the “sample run” that the on-road data was extracted from (i.e. at the very beginning versus in the middle of the sample run) resulted in the discrepancy in PHEV operation between two seemingly similar links.
Table 5.2: ANOVA results showing the response of emissions to link.
Logistically, the data collected on the 10th and 11th streets were systematically different due to how and when the PHEV traveled each roadway. Tenth street data were collected as a continuous part of a larger sampling route as the vehicle was traveling east out of and away from Kansas City’s urban core, whereas the 11th street data were always
collected at the beginning of a sampling run, immediately after a short respite for Semtech zeroing or start of the day calibration. Short periods of vehicle shutdown could result in significantly different use of the internal combustion engine versus the electric motor.
Because of the discrepancy between PHEV power output when traveling 10th street versus 11th street, the “ambient temperature and auxiliary system use” investigation was conducted evaluating data from each street independent from the other.
ANOVA analysis of 10th street data does not reveal a statistically significant difference, at <0.05, between ICE, EM, and total power output under different auxiliary system configurations (the air conditioning on, heater on, or no system use). However, loosening the alpha requirement slightly (α<0.10) yields a statistically significant difference in ICE power output and total power output according to auxiliary system use. Despite the lack of statistical significance at <0.05, qualitative differences are apparent between the power output required to travel 10th street under the use of different auxiliary systems. Air conditioning use required more power output from the internal combustion engine than using the heater or no system at all.
The same analysis for the 11th street data showed no difference between the power outputs required for different auxiliary system use (p=0.797 for total power, p=0.551 for dICE power, and p=0.459 for EM power output). ANOVA analysis limited to evaluating the impact of only heater use (versus no system use) did not yield statistically significant
differences between power requirements with or without the PHEV’s heater. Since little to no difference was apparent between the power demands for no system use and heater use, the ANOVA tests were restructured to evaluate air conditioning use versus no air conditioning use.
Table 5.3: Temperature ranges over which different auxiliary system configurations occurred.
Table 5.4: 10th street ANOVA results.
10th Street
Table 5.5: 11th Street ANOVA results.
The resulting emissions for each link were investigated in a similar manner as the power output. ANOVA analysis indicated that both carbon dioxide (CO2) and nitrogen dioxide (NO2) emissions, evaluated as gram emitted per kilometer traveled, were statistically significant responses to auxiliary system utilization, with air conditioning usage resulting higher overall average emissions. Similarly, Broderick et al., 1995, reported significantly higher vehicle emissions of CO2, NOx, and HC during periods of air conditioning
employment. All other pollutants (carbon monoxide, CO, nitric oxide, NO, and total hydrocarbon, HC) did not demonstrate a significant relationship to auxiliary system use, although more lenient criteria for alpha (α<0.10) would result in statistical significance with carbon dioxide emissions as well. Regardless of statistical significance, aside from
hydrocarbon emissions, quantitatively, AC use resulted in higher average emission of
PHEV dominant control scheme. It is likely that the PHEV’s dICE utilization is a larger dictator of HC emissions than auxiliary system use.
Table 5.6: Compiled 10th and 11th street link ANOVA results specifying emissions (g/m) as a response to auxiliary system use (factor).
CO2 (g/m) None 18 3.420E-04 4.597E-04
0.426 None 18 2.664E-03 2.096E-03
0.264 None 18 1.223E-04 8.550E-05
0.003 None 18 2.786E-03 2.160E-03
0.118 None 18 3.398E-05 5.049E-05
0.242
When the roadway links were analyzed independently from one another, NO2
emissions showed a statistically significant response to auxiliary system use for the 10th street data, but no pollutants were statistically significant responses to system use when traveling along the 11th street link (α<0.05). If, however, the p-value constraints dictating statistical significance were relaxed so that statistical significance were established with a more liberal α<0.10, then carbon dioxide, carbon monoxide, and nitrogen dioxide emissions would also be significant responses to auxiliary system use for the 10th street link. However, except for nitrogen dioxide, no pollutant emissions would demonstrate statistical significance according to auxiliary system use for the 11th street link.
Table 5.7: ANOVA results for pollutant emissions according to auxiliary system use for each link.
10th Street Link 11th Street Link
CO2 (g/m) CO2 (g/m) Heater 3 2.050E-05 3.490E-05 3 1.913E-04 1.882E-04 None 7 9.370E-05 8.010E-05
0.092 Heater 3 7.070E-04 1.225E-03 3 3.249E-03 2.277E-03 None 7 1.802E-03 2.028E-03
0.209 Heater 3 4.910E-05 8.430E-05 3 2.439E-04 1.712E-04 None 7 1.187E-04 9.420E-05
0.021 Heater 3 7.560E-04 1.309E-03 3 3.492E-03 2.446E-03 None 7 1.921E-03 2.108E-03
0.127 Heater 3 2.440E-06 4.227E-06 3 2.156E-05 1.876E-05 None 7 7.138E-06 9.225E-06
0.685
11 5.106E-05 5.800E-06 0.172
Because the PHEV’s air conditioning and heater were only used to maintain operator comfort throughout the study period, their use was directly dictated by ambient temperature.
It is not possible to truly discern between the effect of ambient temperature versus auxiliary system employment with regards to the PHEV’s operation and emissions. Hybrid electric vehicle (HEV) fuel economy has been shown to be a function of ambient weather conditions,
(Fontaras, 2008). Smokers, et al. found that a minute increase in ambient temperature (from 23˚C to 27˚C) resulted in a 7% fuel economy increase. However, when coupled with the decrease in fuel economy associated with air conditioning use, the true effect of ambient temperature on HEV fuel economy is more difficult to discern in real world, on-road studies where air conditioning use naturally occurs with increased ambient temperature. To some degree, the impacts of each phenomenon negate each other. In the case of the PHEV Sprinter’s limited on-road dataset, it is impossible to completely separate the effect of ambient temperature versus auxiliary system use.
Correlations performed on engine power output (ICE, EM, and Total), as well as on emissions were used as a final statistical method to evaluate the impact of ambient
temperature. While system use is definitely related to temperature (air conditioning use occurred at temperatures between 65-102F, with a mean of 82.5F, and heater use occurred at temperatures between 49-51F, with a mean of 50.3F, while the temperature range when no system was required was 44-61F, with a mean of 49.6F), investigating the impact of ambient temperature provides a more continuous scale rather than the three discrete auxiliary-system categories. While on-road sampling throughout the study occurred at a much wider range of temperatures than evaluated here, this analysis was limited to charge-sustaining operation when the PHEV was traveling the selected 10th and 11th street links.
Ambient temperature is weakly positively correlated with ICE operation and less so with total power output (<0.05). Electric motor use did not provide a statistically
significant correlation with ambient temperature. Even if the alpha restrictions were lessened to allowing significance at alpha<0.10, the Pearson’s correlation coefficient between EM power output and ambient temperature is very weak at best.
Table 5.8: Correlation between ICE, EM, and Total power (on a per km
Correlation 0.418 -0.251 0.367 Ambient
Temperature
(˚F) P-Value 0.003 0.089 0.011
As intuitively expected, power (ICE, EM, and total) correlated with all emissions (except for total hydrocarbon emissions). The correlations presented here were performed on data collected only on the specified links during charge-sustaining operation, and are, thus, not necessarily equivalent to similar correlation analysis of the entire PHEV on-road dataset.
Unlike a conventional vehicle, the PHEV can operate using its electric motor alone, its internal combustion engine alone, or a hybridization of both engines. Because of this, the diesel engine routinely turns on and off during normal operation. The presence of several
“cold-starts” during a normal operating cycle has emissions implications for the PHEV that are not at issue with a conventional vehicle. This phenomenon is of interest and will be investigated in future analyses.
Ambient temperature is significantly positively correlated with CO and less significantly with HC emissions, however, no statistical significance was present in the correlation results between ambient temperature and the other measured pollutants for the
Table 5.9: Correlation between emissions (on a per km basis), and ambient
Correlation -0.248 0.583 -0.377 -0.382 -0.381 0.458 Ambient
Temp
(˚F) P-Value 0.322 0.011 0.123 0.118 0.119 0.056
5.3 Conclusions
While PHEV emissions and power output were not universally impacted (in a statistically significant manner) by temperature or auxiliary system operation, some of the measured variables did show a statistically significant response to ambient temperature and auxiliary system use. Because of this potentially confounding issue, all subsequent analyses are conducted using on-road data collected when no auxiliary system was in operation, and the ambient temperature range was, consequently, more moderate. Appendix C provides the comprehensive tables detailing the PHEV Sprinter’s emissions and power output during different auxiliary system employment used in the previous discussion.
Chapter 6: Vehicle Specific Power Modal Analysis