Proceedings of the 13 th International Conference on Environmental Science and Technology Athens, Greece, 5-7 September 2013 ON-ROAD REAL DRIVING AND ROAD GRADIENT DATA PROCESSING METHODOLOGIES TO FORM DRIVING CYCLE COMPLETE DYNAMOMETER TESTS E.G. TZIRAKIS 1 and F.E. ZANNIKOS 1 1 Laboratory of Fuels and Lubricants Technology, School of Chemical Engineering, National Technical University of Athens, Iroon Polytechniou 9 15773 Zografou Athens. e-mail: vtziraks@central.ntua.gr EXTENDED ABSTRACT When a Driving Cycle (DC) is applied on a laboratory chassis dynamometer, it simulates the driving conditions in the road network of modern cities. These models for dynamometer control software are used for exhaust emission and fuel consumption measurements and they find broad application in research for environmental pollution, energy conservation and alternative automotive fuels (biofuels). Furthermore, DC tests on chassis dynamometer simulate a flat road ignoring the effect that Road Gradient (RG) has on exhaust emission and fuel consumption. This work is a part of a project which involves the development of Real World DCs from on-road driving and road gradient data from the greater area of Athens, Greece. Two different methodologies of DC development are presented here, based on Matlab code. Driving patterns from various test vehicles were processed to form DCs of specific number of driving periods (phases). The first method (A ) was applied for short duration and limited number of driving periods. The second method (B ) was suitable for long duration and multi-driving period cycles. The main criterion for the design and acceptance of each method was the correlation of the resulted DCs with the corresponding characteristics of the processed road data. Time is an important issue when processing data. Method A is time consuming but gives very accurate results requiring small amounts of data. In opposition, method B a larger amount of data is needed for acceptable results but the processing time is extremely short. Applying both methods on the same set of data, method A needed 5 hours to complete the processing instead of the 9 seconds of method B. The corresponding average accuracies were 99.9 and 97.2 respectively. RG is a parameter that seriously affects vehicle s exhaust emissions and fuel consumption especially in a city like Athens with unique road network topography. RG was taken into account for the development of the DCs for both methods. For method A, RG was included in the final result by intervention on the mean positive and negative accelerations by the one that results from gravity on an inclined road. For method B, RG was assimilated by a Load Cycle expressed in kw and used in a combination with the developed DC, thus forming a complete chassis dynamometer test. Load Cycle (LC) was produced by calculating the power related to vehicle s mass that holds or assists it when driving uphill or downhill. Complete test that include RG were developed for both motorcycles (Greek Urban Driving Cycle for Motorcycles) using method A and passenger cars (Greek Urban Driving Cycle) using method B. KEYWORDS: Driving cycles, road gradient, chassis dynamometer, emissions, fuel consumption.
1. INTRODUCTION In order to evaluate the environmental impact from vehicle emissions, researchers have developed Driving Cycles (DC) used for laboratory chassis dynamometer emission testing. DCs are speed-time profiles simulating driving conditions on road networks, either urban or not. Road network topography and driving style are important factors directly associated with driving conditions. Road Gradient (RG) should be taken into account when estimating exhaust emissions and fuel consumption, as well as the driving behaviour which is mostly aggressive in modern cities (OECD, 2004), (Tzirakis et al., 2007). In addition, motorcycles constitute a significant share of vehicle fleet in southern European countries and especially Greece. Due to their differences in speed profiles compared to cars, driving cycle development for motorcycle emission testing is considered essential (Chen et al., 2003). The vehicle fleet is changing in terms of technology as manufacturers need to continuously reduce their fleet average CO 2 emissions to the levels of 95gr/km by 2020 (European Commission, 2010) (measured on NEDC). All these impose the need for driving cycles updating in order to review legislation cycles. Legislative DCs, are used for Vehicle Emission Certification and are imposed by governments. Non-legislative cycles such as the Hong Kong driving cycle (Hong et al., 1999), and the Athens driving cycle (ADC) (Tzirakis et al., 2006) are a useful tool in research for energy conservation and pollution evaluation through the vehicle testing on exhaust emissions and fuel consumption (Andre et al., 2006) as well as in the field of vehicle design tooling and marketing. Since Real world driving cycles reflect more accurately the urban driving conditions, they support research on fuel behaviour comparison, alternative and renewable fuels (i.e. Biodiesel), fuel reformulation, fuel additive development and engine modification (Karavalakis et al., 2007). Each city has a unique driving profile and the collected traffic data result in a different driving cycle depending on characteristics such as the road network, the driving behaviour and the vehicle fleet potentiality and number (Andre et al., 2006). As recently mentioned, in lab real driving emissions are determined in order to establish the emission limits for EURO VI (Hausberger et al., 2012). Comparison of results from chassis dynamometer tests with NEDC and Real World driving cycles based on traffic data from European and non- European cities showed significant differences in emission and fuel consumption levels (Karavalakis et al., 2007). Real world driving data and chassis dynamometer tests contribute on developing models which calculate CO 2 emissions and fuel consumption directly using a set of vehicle fleet characteristics. There are a series of well known developed models that can be applied in various countries and cities around the world (Robin and Ntziachristos, 2012). Furthermore, aggressive driving is as fuel consuming and emission surcharging as driving uphill as the use of excessive throttle accelerator is a necessity on both driving conditions (Tzirakis et al., 2007). The DC construction methodologies include techniques such as the chase car method using instrumented vehicles, route selection or complete road network coverage of specific areas and cycle construction where a great number of DC characteristics should meet in the largest percentage possible, the corresponding characteristics of the on road data. Sometimes the process was to the extent of the whole trip a vehicle was performing and some others to the extent of its stop to stop driving phases (Hung et al., 2007). For the monitoring of European traffic characteristics and the formation of representative driving cycles, topnotch institutions are involved (Andre, 2004). 2. DATA COLLECTION PROCEDURE A significant parameter when developing a real world DC for a specific city or area is whether the road gradient is included in the bench test. DCs are usually developed assuming that test vehicles are driven on zero gradient and therefore, when a vehicle is tested on a chassis dynamometer it is assumed that it is driven on a flat road. In cases
where the topography plays a significant role on the speed profile of the vehicles, it is essential that the road gradient is included in the final dynamometer test, through the DC. (a) (b) Figure 1. Change of the average value of average speed (a) and stop % time (b), during data collection progress as well as of the reliability level of 95%. For a general work frame, the peculiar area of Athens was selected for the collection of on-road data with the aid of instrumented vehicles. The procedure followed for the development of DCs comprises of three major components, the logging technique, the route or area selection and the cycle construction methodology (Tzirakis and Zannikos, 2011). They include the use of sophisticated equipment which is rearranged in order to record the desired vehicle data and the routes or area covered in the Attica basin for the data collection. It was mandatory to ensure that the samples collected for every driving cycle to be developed, are statistically sufficient. For this purpose graphs were created displaying the sample collection sequence for two of their main characteristics (Figure 1). 3. DRIVING CYCLE DEVELOPMENT METHODS The concept of developing a complete DC dynamometer test is described in this paper. Two methods were determined using mainly Matlab code. This paper describes how road gradient is taken into account when developing a DC for both of the methods by using a different approach. Method A (Tzirakis and Zannikos, 2011), (Tzirakis et al., 2008), was used to develop the Greek Urban Driving Cycle for Motorcycles (GUDCM) and method B for developing the Greek Urban Driving Cycle (GUDC) for passenger cars. 3.1. Method A The first three steps of the methods are identical. Data which were collected in second intervals were processed in the form of stop-drive-stop phases. A marginal 5% of the total number of phases was removed considering 5 basic characteristics. Phases were separated in groups according to their duration. The procedure followed according to method A can be seen in Figure 2(a). A capable number of phases were selected from each group according to their duration starting from the mean value of the duration of each group. The number of phases selected depends upon the accuracy and the number of the input criteria and the capability of the computer used to do the combinations. The selected phases are then used for a very large number of combinations, which depends on the number of the groups and phases selected from each group, which are performed in order to have the desired result of which the basic characteristics (input criteria) will be as close to the characteristics of the road data as possible. The basic criteria where based upon a number of average values of the on-road data which must agree with those of the final cycle. Those criteria are: % stop time, % positive acceleration, % negative acceleration, average speed without the stops, average positive acceleration and average negative acceleration.
Road data in the form of driving phases (microtrips) Road data in the form of driving phases (microtrips) Removal of 5% of the marginal phases according to the criteria: Separation in equal, as far as the number of phases is concerned, groups according to the duration of the phases Duration Distance Average speed Maximum speed Average acceleration Removal of 5% of the marginal phases according to the criteria: Separation in equal, as far as the number of phases is concerned, groups according to the duration of the phases Duration Distance Average speed Maximum speed Average acceleration Selection of capable number of phases according to duration, around the mean value of each group Criteria input for the driving cycle selection depending on the needs Estimation according to the minimum summation of the deviation percentages from the mean value of the groups for each criterion Change of the input criteria depending on the result Phases combination =0 or Phases combination >1 Combinations with the phases on from each group at a time for the formation of the cycle and checking according to the criteria Phases combination =1 Selection of the specific phases which constitute the Average Driving Cycle of all data used in the process Driving Cycle - no road gradient included Test vehicle mass Assimilation of the RG on chassis dynamometer ( Load Cycle ) Final Dynamometer test (Driving Cycle-road gradient included by incrementing on positive and negative acceleration) (a) Final Dynamometer test (Driving Cycle-road gradient included (b) Figure 2. Methods developed for DC construction (a) Method Α (b) Method Β. RG can be included in the results when using method A by incrementing to mean values of positive and negative acceleration and gives accurate results even though the acceleration values are changed in order to include road gradient. The additional acceleration (α add) that a vehicle wastes when driven on an inclined road could be easily calculated. It is the component of acceleration of gravity parallel to the road surface: α add = g (H 2-H 1)/d (1) Where g is the acceleration of gravity, d is the distance covered for 1 second (data logging interval=1hz) and H 2-H 1 is the altitude difference between the logs.
3.2. Method B This method is a very quick statistical evaluation of the driving phases according to the input criteria, which can be performed only by using programming. It gives only one result (see step 6 Figure 2(b): Driving cycle no road gradient included ) which is a combination of phases of different duration. The basic characteristics (input criteria) of the result match, in the greatest percentage possible, the corresponding characteristics of the on-road data. The accuracy increases as the population of the data increases. This method also gives the capability of including road gradient through positive and negative acceleration by incrementing on those criteria. This however, reduces the accuracy of the resulted driving cycle in relation to the rest of the criteria that come from on-road recordings. In addition to that the driving cycle is becoming more aggressive. The accuracy can be increased either by collecting more data to be entered to the programme or by including road gradient in the chassis dynamometer test using the method described in Figure 3 and in the lines of the next paragraph. Isolation of the recorded altitude that relates to the phases of the driving cycle Estimation of the road gradient in %. Adjustment of the altitude characteristics in terms of the on road data: % uphill % downhill Mean gradient ascending Mean gradient descending Impedance and assistance rolling estimation in kw. Assimilation of road gradient on the chassis dynamometer: ( Load Cycle For Method B ) Figure 3. Procedure followed for the assimilation of road gradient. In order to assimilate the load on a chassis dynamometer, the recorded altitude that relates to the selected phases that will be used to form the final driving cycle were isolated. Proper modification was made in order that the gradient to match the mean gradient of all data recorded. In addition to that, attention was paid to a great extend on the increases and decreases of gradient, so that the final driving cycle will be easy driven. For the development of a Load Cycle that will be used on a chassis dynamometer in order to assimilate the road gradient, the power of the vehicle that relates to its mass due to gravity on a road with slope must be calculated. This power can be either positive (downhill) or negative (uphill). Power (P gradient) can be calculated as: P gradient = g v sinφ m vehicle (2) Where g is the acceleration of gravity, v is the velocity of the DC, φ is the road gradient of the specific phases of the derived driving cycle and mvehicle the mass of the test vehicle. For gradients below 5 o it is assumed that: sin(φ) = tan(φ)=φ. Therefore, the gradient can be written as: φ = (H 2-H 1)/d (3) Where d is the distance covered for 1 second (data logging interval=1hz) and H 2-H 1 is the altitude difference between the logs. For the data logging of 1Hz stands: d=v.t = v so (1) becomes: P gradient = [(H 2-H 1)/v] g v m vehicle=(h 2-H 1) g m vehicle (4) 3.3. Comparison of methods For comparison purposes, both methods were applied on the same set of data creating two simple speed-time profiles, without using the option of RG (Tzirakis et al., 2008). As seen in Figure 2, the methods differ from each other only in the final stage of driving cycle formation, through the phase combination. The phase groups used are the same for both methods. The resulted cycles have both similarities and dissimilarities. Time is an important issue when processing data. Method A is time consuming (5 hours) but gives
very accurate results. Many of the characteristics of the DCs, shown on Table 1, match over 99% the corresponding characteristics of the on-road data. Using the B method, the resulting cycle is acceptable since the smallest match percentage is 95.57%. Despite the acceptable percentage the time needed for the computer to process the data was extremely small (9 seconds). Table 1. Comparison of matching percentages in 6 characteristics of DCs with the corresponding of the on-road data, developed through two different methods using the same set of data. Method A Method B Time consumed for processing (computer) 5 hours 9 seconds Cycle duration 100% 97.44% Average speed 99.76% 97.09% Average speed (without stops) 99.79% 96.95% % stop time 99.56% 99.26% Average positive acceleration 99.93% 97.30% Average negative acceleration 100% 97.85% 4. RESULTS Figure 4 illustrates the Greek Urban DC for Motorcycles (GUDCM) compared to a DC developed without taking into account the road gradient. These cycles were developed using method A. When road gradient is included through acceleration the cycle is more aggressive resulting in higher travelling speeds. Figure 4. GUDCM compared to DC with no road gradient taken into account Table 2. Basic DC characteristics compared to those of the recorded data. Characteristic Data Average GUDCM DC No Road Values Gradient Duration(s) 822 822(100%) 822 (100%) Moving time (s) 723 723(100%) 723 (100%) Average speed (km/h) 29.04 28.86 (99.38%) 29.05 (99.96%) Stop time (%) 12,07 12.04 (99.75%) 12.04 (99.75%) Av. positive acc. (ms -2 ) 0.6045 (0.5398) 0.6065 (99.67%) 0.5411 (99.76%) Av. negative acc.(ms -2 ) 0.6497 (0.5598) 0.6349 (97.72%) 0.5629 (99.45%) Table 2 shows the basic characteristics of GUDCM and the one developed without taking road gradient into account as well the corresponding values of the recorded data. Average positive and negative acceleration without road gradient taken into account is written inside the brackets of Data Average Values column. When no RG is included all basic characteristics match over 99.5 % with the data average values. When RG is included the corresponding percentages are also over 99.5 % with the exception of negative acceleration (97.7%). This is due to the changed values of positive and negative acceleration in the programme s input data in order to include RG, which tries to combine the new values of acceleration with the rest unchanged characteristics. Method B was
used for the construction of GUDC. The complete driving cycle test that includes the load cycle is illustrated in Figure 5. Figure 5. GUDC and assimilation of road gradient by creating a Load Cycle. Table 3. Speed and load points of GUDC for a ten second section of the cycle. Time speed (km/h) power (kw) Time speed (km/h) power (kw) 1044 24.96-3.673 1047 34.47-1.476 1045 28.26-3.133 1048 37.87 0.000 1046 28.69-2.079 1049 35.78 1.364 Table 4 shows the main characteristics of GUDC compared to other driving cycles. Table 4. GUDC compared against recorded data and against other driving cycles. Characteristic Data Av. ECE- Artemis GUDC NEDC Values 15 Urban Duration (s) 1180 1175(99.58%) 1180 195 993 Average Speed (km/h) 18.94 18.67(98.57%) 33.6 18.4 17.65 Av. Speed-no stops (km/h) 27.76 27.31(98.84%) 44.8 26.5 24.66 Stop time (%) 31.76 31.66(99.69%) 25.42 30.8 28.4 Positive Acceleration (%) 33.89 33.89(100%) 20.93 21.5 34.64 Av. pos. acc.(ms-2) 0.686 0.692(99.13%) 0.726 0.642 0.732 Distance (m) 6207 6094(98.18%) 11007 990 4870 No of Phases 19.62 20(98.10%) 13 3 22 5. CONCLUSIONS Method A has the advantage of giving acceptable results with a small amount of recorded data. Additionally it is not affected when changing the average values of positive and negative acceleration in order to include road gradient in the resulted driving cycle. On the other hand, the time needed to give results is mainly affected by the number of phases of the cycle to be developed so it is suitable for cycles with small number of phases. The accuracy in Method B is greatly affected when road gradient is included in the process by incrementing on the criteria of acceleration. The computer however needs only a few seconds to give the final result. That is the reason why the process of including the road gradient using the Load Cycle was created. More criteria could be added in the programs in order for the resulting driving cycles to be more representative in relation to the characteristics of the road data. However, this could lead to their low accuracy which can be improved with a larger set of data grows. Gathering of on-road data can be done remotely through GPS technology and telemetry or with the help of really fast data transferring 3G and lately, 4G mobile networks. A new application that can be run in a smart phone or tablet is now under development, will log the vehicle s operation parameters directly from the CAN BUS and send it in real time at the laboratory s data base. Real world driving cycles are extremely useful for policy makers and scientists on
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