Speakers Information- Controls, Measurement & Calibration Congress
Ethanol Impact in Objective Drivability Evaluation Edgard Marcelo de Assis Eduardo Nunes da Silva Douglas Martini Ford Motor Company
ABSTRACT In the competitive environment of the automotive industry, the search for efficiency and quality differential requires the development of verification processes that leverages both aspects. For the Brazilian market, a further complexity is added to the powertrain controls and calibration development and verification process, the requirement to design and verify the powertrain calibration considering the two fuels available in the market, gasoline E25 and ethanol E100. In this paper, a study is presented analyzing the difference of the drivability response, measured with the AVL Drive, between gasoline and ethanol. Statistical methods are applied to analyze the data and identify the significant differences.
INTRODUCTION It is almost a mandatory a requirement for the vehicle manufactures to design their products to be capable to run with the Brazilian gasoline E25 and ethanol E100 to compete successfully in the market. The labels E25 and E100 stand for the percentage of ethanol content in the fuel. The gas stations in Brazil are equipped with at least 2 fuel tanks, one for each fuel, where the customer can decide in the pump which fuel, he or she is going to fill his or her car. The development of flex fuel vehicles and the impacts of the ethanol usage has been a theme for several technical articles in Brazil since the second ethanol boom in 2003, when the manufactures started to produce flex fuel vehicles with the technology to detect the fuel type in the tank by the exhaust gases sensor feedback. The capability to automatically detect the fuel and to adapt to it was the key feature to overcome the Brazilian trauma about ethanol created in the 80’s, the lack of ethanol fuel in the market. With the lower average cost of ethanol fuel in Brazil, the flex fuel vehicles brought an economic benefit to the cars owners, but in the other hand, it also brought an additional complexity to the manufactures as they now have to design and verify powertrain systems with both fuels. In the controls areas, the engine control units (ECU) and software had to be redesigned to ensure the engine attributes optimization as performance, fuel economy and emissions. In the calibration area, the testing requirements more than doubled as the calibration needs to be designed and verified for the both fuels and their blends. An attribute that is affected by the ethanol and theme of this work, is the drivability. Drivability can be understood as the capacity of the engine to deliver the torque requested by the driver, at the ratio and time expected pleasantly. It is often evaluated subjectively, but can also be quantified objectively through accelerometers. Problems such as hesitation, powertrain excitation during acceleration (accelerator pedal tip in) and deceleration (accelerator pedal tip out) maneuvers are identified in this attribute. This paper focuses in objective drivability measurement using the commercial measurement system AVL DRIVE, which quantifies the drivability in several criteria during a standard drive evaluation procedure. The great advantage of the AVL DRIVE system is the possibility to record several data for each criterion, visualize their variation and apply descriptive statistics. Besides the descriptive statistics, this paper study the difference, among the criteria when the vehicle is tested with gasoline E25 and ethanol E100. For the differences study, it is applied the inferred statics concepts using the MINITAB software.
MAIN SECTION ETHANOL INFLUENCE – The Brazilian market demands Flex-Fuel vehicles, which can be fueled with both ethanol and gasoline, represented by the abbreviations E100 and E25. The Brazilian E100 is composed of 92.4% pure ethanol and 7.6% water, also called ethanol hydrous, and E25 is blended from 75% pure gasoline and 25% pure ethanol without addition of water. Both fuels have physicochemical characteristics very different. These differences require almost a unique combustion calibration for each fuel. The table 1 shows the fuel properties of both fuels.
Parameters Representation Fuel Lower Heating Value
Unit – kJ/kg kJ/litro kg/litro
Gasoline Ethanol (CH1.78)n C2H5OH 43.5 28.225 32.18 22.35 0,72 – 0,78 0,792
Densidy Octane RON – 90 - 100 102 - 130 (Research Octane Number) Octane MON – 80 - 92 89 - 96 (Motor Octane Number) kJ/kg 330 - 400 842 - 930 Latent heat – 14,5 9,0 Stoichiometric A/F Kpa 40 - 65 15 - 17 Steam pressure °C 220 420 Ignition temperature Water solubility % volume ~0 100 Source: API (1998) Goldember and Macedo (1994).
Table 1: Fuels Properties Analyzing the table, it can be observed that gasoline has a better capability to produce more power and thus, a greater advantage in terms of fuel consumption, reaching up 30% more efficiency than ethanol. However its Octane RON (detonation resistance while operating in medium to high load and engine speed up to 3000 RPM) and MON (detonation resistance while operating in full load and high engine speed conditions), shown in table 1, are lower than ethanol, being necessary in some conditions to limit the spark advance to prevent detonation. Ethanol has a higher resistance to the detonation phenomena in low and high loads and in different rotations, enduring higher compression ratios, which increases the temperature of the combustion chamber, without having spontaneous combustion; thus ethanol has capability to have a better thermal efficiency and generate more torque than gasoline in these conditions, without being limited by the engine knocking. NAKATA et al. (2006) studied the ethanol effect in the engine produced torque and thermal efficiency in his paper and demonstrated that despite of the lower heat energy, the ethanol allowed the engine to operate with higher spark advance, which increases the thermal efficiency and increases the output torque.
Figure 1: Torque, Ignition and volumetric efficiency
Figure 2: Thermal efficiency vs. spark advance With higher output torque, it is expected the engine to generate higher accelerations during a tip in maneuver and also, a higher input force to excite the powertrain system. But in constant accelerations, it is not expected a difference in the engine behavior. Another condition of relevant difference between E25 and E100 fuels is on cold start, situation that ethanol takes significant disadvantage compared to gasoline. Ethanol at temperatures below 13 °C cannot generate combustion without auxiliary device as the gasoline injection or the ethanol heating systems, therefore, it is not the intention of this paper to compare the cold start performance, but to analyze the drivability differences at stabilized engine temperatures. The expectation of this work is to learn the influence of ethanol in the excitation of the powertrain system and its dynamics response. Also to learn if the differences in the fuel control software and calibration, for example the fuel transient phenomena control, which is the condensation of the fuel in the admission runner walls and valves interferes in the drivability response. DRIVABILITY MEASUREMENT SYSTEM – The AVL-DRIVE is an equipment offered commercially that provides the drivability assessment in drive rate (DR) units. The software computes the signals from the engine and from accelerometers installed in the vehicle that are recorded during specifics driving conditions. Then, the software returns a drive rating, from 1 to 10, for approximately 470 criteria used to score the overall drivability. The drive rating correlates to the customer expectations, similar to a subjective evaluation, the higher the score, the better the drivability. The Figure 1 illustrates a summary of the drive rating description.
Figure 3: AVL-DRIVE Rating Once installed the equipment, the test procedure consists in driving the car following a maneuver matrix that serves as a drive guide. The maneuvers are full load accelerations, tip ins, and tip outs. They allow the AVL-DRIVE software to detect the operation modes and score the drive rate. The software can return a real time drivability assessment, although, a data post processing is required to scrub the nonvalid scores caused by signal noises like rough road, lateral acceleration during turns, etc. The total drivability index, which is the overall vehicle drivability assessment, is composed by the weighted sum of several main operation modes rates. Each of the main operation modes is also composed by the weighted sum of several suboperation modes, which are composed by the weighed sum of the averages of the criteria rates. To build this work, not all the 470 criteria were analyzed, but it was selected 44 criteria from 6 sub-operation modes of 3 main operation modes to be studied. The selection were based on criteria that evaluates maneuvers that occurs during transient conditions as tip in and tip out and that can be easily correlated with the subjective evaluation. The Figure 2 exemplifies the criteria, sub-operation modes and main operation modes hierarchy.
Figure 4: Drive rating hierarchy It was measured 8 vehicles from different cars manufactures with the 2 fuels, E25 and E100 having for each criterion, 2 sets of data. The 2 sets are then statistically analyzed, testing the difference between E25 and E100 response.
STATISTICAL ANALYSIS – The statistical analysis of the data is done by the hypothesis test comparing the medians, or means of the E25 and E100 collection of drive rates. For each of the sub-criteria, the E25 and E100 samples, containing the drive rates data are statistically compared following two methods according to the type of the distributions. The method for the hypothesis test depends on the type of data par, if they follow a normal distribution or not. When the data follows a normal distribution, they are called parametric data and non-parametric if they do not. The AndersonDarling normality test is performed in all the 704 samples to verify whether the data resembles the normal distribution. 2
The Anderson-Darling test is a goodness of fit test denoted as A that tests the conformity of the distribution against a 2 specified one. The A can be obtained from the equation, where “z” is the cumulative distribution function:
1 n A 2 = − ∑ (2i − 1)(ln ( z i ) + ln(1 − z n +1−i )) − n n i =1 The test then is compared with critical values of the theoretical normal distribution. The Anderson-Darling tests performed using MINITAB. The tests showed that the majority the samples follows a nonnormal distribution and only about 7% of the samples pars, or the E25 and E100 samples together for each sub-criteria, are parametric. Understanding the type of distribution is important as the inferred statistics is based on the assumption the normal distributions. The tests as ANOVA and t-test will not be only valid in the data that are not parametric. Despite of the data were recorded randomly, there are events that skew the distribution resulting in a non-parametric data. There are outliers and uncontrolled factors events caused by variations the sub-system properties like transient fuel compensation, air/fuel ratio errors, mounts rubber temperature, clutch temperature, ambient temperature, driver pedal input, etc. The causes for the non-normal distribution are also not studied in this work. The analysis is carried on with the assumption that the data is totally representative of what the customer will experience when driving the cars. The hypothesis test selected for the normal distributed data was the 2-Sample t test, where the means of E25 and E100 data are tested. For the non-parametric data, it was selected the Mann-Whitney test, which is equivalent to the 2-Sample t. Due to the characteristics of the non-parametric data, the medians are used in the comparisons instead of the means. The Mann-Whitney test is used to test if the medians for 2 samples with similar variance are different. The null hypothesis H0 always considers the medians being equals while the alternative hypothesis Ha considers them to be not. The hypothesis statements become: • •
H0: ME25 = ME100 (non-parametric data) and µE25 = µE100 (parametric data) Ha: ME25 ≠ ME100 (non-parametric data) and µE25 ≠ µE100 (parametric data)
The tests that returned a p-value greater than 0.05 (alpha) mean they failure to reject the null hypothesis, but in order to simplify the results comprehension, we are referring to them as they have “equal medians”. The same approach is applied to the tests that returned a p-value lower than 0.05 (alpha), instead of referring to them and they rejected null hypothesis, we will just refer to them as they have “different medians”. Risks α and β – The sample size and the standard deviation determines the risks α and β. The risk α is the risk of rejecting the null hypothesis, or finding a difference when it does not exists, in all tests, the probability to end in this error is of 5%, this is referred as the error of Type I. In similar way, the risk β is the risk of accepting the null hypothesis, or not see a difference when it exists. In this case the probability varies according the sample size and the sample standard deviation. This is referred as the error of Type II. RESULTS – As described previously, the objective of the study is to identify statistical difference between the drivability response of E25 and E100. To accomplish the goal, it was measured the drivability using an objective measurement system.
The statistical hypothesis tests were performed on the 704 samples generated by the measurement and the results were compiled to visualize the trend among the tested vehicles. It was possible to observe that 40% of the criteria have a difference between E25 and E100, although, not all of the vehicles tested follows the same pattern. It was observed that only 7 criteria showed the similar pattern: • • • • • • •
Acceleration full load expected acceleration; Acceleration partial pedal load rising expected acceleration; Acceleration partial pedal load rising reference acceleration; Tip in after constant pedal absolute torque; Tip in after closed pedal absolute torque; Tip out after acceleration kick; Tip out after acceleration initial bump.
The “acceleration full load expected acceleration” criterion showed in the figure 5 is the mean of the longitudinal acceleration difference between the real acceleration and the theoretical acceleration. The “acceleration partial pedal load rising expected acceleration” criterion showed in the figure 6, compares the longitudinal acceleration picks of the real acceleration and the theoretical acceleration during a slow tip in. It was possible to observe a trend of the E100 data to have better scores than the E25, which correlates with NAKATA et al. (2006) study, as the ethanol is not sensible to the knocking spark advance limit variation as function of the intake air temperature, the output torque from the engine is not deteriorated. The “acceleration partial pedal load rising reference acceleration” criterion showed in the figure 6 is similar to the “expected acceleration” criterion, but in this case, it compares the pick longitudinal acceleration with the best in class vehicle reference. Despite of the clear difference among the tested vehicles, the separation is not so wide and the difference between the medians of 0.1 DR.
Figure 5: Acceleration full load analysis
Figure 6: Acceleration partial load rising pedal analysis
The tip in “absolute torque” showed in the figures 7 and 8 is the inferred engine torque from the vehicle longitudinal acceleration, the higher the inferred torque, the higher the rating. In this criterion, there is also a trend for the ethanol to have higher scores, but again, not so wide with a difference between the medians of 0.1 DR.
Figure 7: Tip in after constant pedal analysis
Figure 8: Tip in after closed pedal analysis
The “initial bump” showed in the figure 9 is the maximum acceleration gradient in the chassis after a change in the accelerator pedal position, The higher the acceleration gradient, the lower the rating. It was observed that only the tip outs after acceleration showed a significative differece between E25 and E100, where the E100 has the worse medians, or higher torque disturbances. The “kick” criterion also showed in the figure 9 is the fist half period chassis acceleration amplitude after a change in the accelerator pedal position (a graphical explanation is provided in the appendix), the higher the amplitude, the lower the rating. This criterion is also associated with the torque disturbances and powertrain system excitation.
Figure 9: Tip out after acceleration analysis
Figure 10: Tip out after constant speed analysis
The figure 10 completes the compiled evaluated sub-operation mode charts, although, it was not observed differences between the fuels in any of its criteria.
CONCLUSION The paper proposed a study of the impact of ethanol in the vehicle drivability by the observation of the statistical tests trend among a group of flex fuel vehicles. The source of the statistical samples was the drive rates (DR) generated by the objective drivability measurement system AVL-DRIVE.
The objective drivability measurement system presents a great advantage as it can record thousands of events to allow descriptive statics analysis and of course, understand the physicals in the low scored drivability events. The AVL-DRIVE criteria selected are evaluations that occur during transient maneuvers, representative of the customer experience during a regular drive. They capture longitudinal acceleration and torque disturbances. From the results, it was concluded that: 1. The objective drivability system scores different rates for ethanol and gasoline. The difference in the rates was noticed the 3 main operation modes selected for this work, acceleration, tip in and tip out. The difference is caused by the criteria that measure the engine torque and longitudinal acceleration; 2. The compiled data trend confirms that the vehicles in Brazilian market have the controls and calibration optimized, in the performance aspect, to run with ethanol fuel and the ethanol, impacts positively in the objective drivability measurements due to the higher engine output torque; 3. The torque disturbances during the tip in with gasoline and ethanol are in the same level and there is no impact of the ethanol fuel; 4. Only the tip outs after acceleration are impacted by the ethanol caused by the higher powertrain excitation forces.
REFERENCES 1. Koichi Nakata, Shintaro Utsumi, Atsuharu Ota, Katsunori Kawatake, Takashi Kawai and Takashi Tsunooka. “The Effect of Ethanol Fuel on a Spark Ignition Engine”. SAE TECHNICAL PAPER SERIES, 2006-01-3380, 2006; 2. Kenneth Kar and Wai Cheng, Kaoru Ishii. “Effects of Ethanol Content on Gasohol PFI Engine Wide-Open-Throttle Operation”. SAE PAPER, 2009-01-1907, 2009; 3. GOLDEMBERG, J. & MACEDO, I. C. ”The Brazilian Alcohol Program – An overview”. Energy for Sustainable Development, v. 1 (1), 1994. 4. ASSIS, E. M.; KURAUCHI, R.; CARLOTI, M.; RIBEIRO, J. C.; LON, D. L.; CASTRO, A. Drivability Improvements on Electronic Diesel Engines. SAE TECHNICAL PAPER SERIES, 2003_01_3656, 2003; 5. BASS, Issa. Six Sigma Statistics with EXCEL AND MINITAB. First Edition. New York, NY, USA: McGraw-Hill, 2007. 386 p. 6. Sleeper, A. D. Design for Six Sigma statistics 59 tools for diagnosing and solving problems in DFSS initiatives. New York, NY, USA: McGraw-Hill, 2006, 854 p. Six sigma operational methods series; 7. Urdan, T. C. Statistics in Plain English. Second Edition, Mahwah, NJ, USA: Lawrence Erlbaum Association, 2005, 184 p; 8. DODGE, Y. The Concise Encyclopedia of Statistics. Springer, 616 p.
CONTACT Edgard Marcelo de Assis Mechanical Engineer Email: eassis2@ford.com
Douglas Martini Computer Engineer Email: dmart253@ford.com Eduardo Nunes da Silva Business Management Bachelor Control and Automation Engineering Student Email: esilv280@ford.com
ADDITIONAL SOURCES AVL-DRIVE software help.
DEFINITIONS, ACRONYMS, ABBREVIATIONS ANP Agência Nacional de Petróleo α: alpha, probability to occur the error of Type I in the hypothesis test β: beta, probability of occur the error of Type II in the hypothesis test DR: Drive rating, unit from the AVL-DRIVE equipment E25: Brazilian gasoline fuel with 25% percent of ethanol E100: Brazilian ethanol fuel, which content is 93% of ethanol and 7% of water ECU: Engine Control Unit H0: Null hypothesis Ha: Alternative hypothesis ME25: Median of the E25 sample µE25: Mean of the E25 sample MON Motor Octane Number n: Sample size RON Research Octane Number RPM Rotation per Minute Tip in: Maneuver of pressing the acceleration pedal Tip out: Maneuver of stepping out the accelerator pedal WOT: Wide-Open-Throttle operating condition
APPENDIX KICK CRITERION DEFINITION – The fist half period chassis acceleration amplitude after a change in the accelerator pedal position. The higher the amplitude, the lower the rating.
Figure 11: AVL-DRIVE “kick” criterion INITIAL BUMP CRITERION DEFINITION – The maximum acceleration gradient in the chassis after a change in the accelerator pedal position, The higher the acceleration gradient, the lower the rating.
Figure 12: AVL-DRIVE “initial bump” criterion