Effect of driving patterns on fuel-economy for diesel and hybrid electric city buses

EVS28 KINTEX, Korea, May 3-6, 2015 Effect of driving patterns on fuel-economy for diesel and hybrid electric city buses Ming CHI, Hewu WANG 1, Minggao OUYANG State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing,China, 1 Corresponding author: wanghw@tsinghua.edu.cn Abstract Driving pattern parameters were of great importance not only to the construction of the driving cycle but also to the energy control strategy for city bus. This study aimed at investigating which driving pattern parameter had a main effect on the fuel consumption of two kinds of buses, conventional diesel bus and hybrid electric bus (HEB). The driving data were collected in Nanjing city for the conventional diesel bus and in Hengyang city for the HEB. The speed and fuel consumption were measured second by second under real world public service for several whole days. Main 15 driving pattern parameters, such as the average speed and driving speed, average acceleration and deceleration, time proportions of different speed intervals were calculated. The relationship between the fuel consumption with these driving pattern parameters were analysed using regression method. The data showed that the fuel economy were 42.4L/100km and 32.4L/100km for diesel bus and HEB respectively, the main driving parameters were largely different for diesel bus operation and HEB operation. The analysed results showed that, the fuel consumption was mainly effected by the proportions of different speed intervals and the percentage of idling time for diesel bus, and mainly by the average speed and average acceleration for the HEB. The regression equations with acceptable accuracy showed its potential to predict the fuel consumption while the driving pattern parameters were estimated in different real world traffic conditions both for diesel and hybrid electric buses. Keywords: driving pattern parameters, fuel-economy, city buses, hybrid electric bus 1 Introduction Some research showed that the driving pattern affected the fuel consumption and emissions of vehicles, especially the passenger cars, together with other influence factors [1, 2]. Some derived characters, named driving pattern parameters, were usually used to explain the information of different driving patterns [2]. Thus the effects of driving pattern parameters on fuel consumption had been studies and results showed that the influence of key parameters, such as speed/acceleration and road grade, on the fuel consumption were clearly large. Ericsson Eva [2] explored 64 driving pattern parameters influence on fuel-use. In which, nine driving pattern parameters of great impact on the fuelconsumption were found and one regression EVS28 International Electric Vehicle Symposium and Exhibition 1

equation was used to model the fuel consumption. The VSP (vehicle specific power) -based approach was researched by H. Christopher Frey et al and Haikun Wang et al[3,4] to analyse the sensitivity of the fuel-use to acceleration for a traditional vehicle. Michel Andre s research showed that the speed and acceleration constituted a good basis for emission modelling [5]. In this paper, driving data in two cities for two kind buses were collected for several days. Based on these data, 15 driving pattern parameters were calculated, which included average speed, average driving speed, average acceleration, average deceleration, proportion of speed interval. In the third part, the regression analysis on the relation between driving pattern parameters and the fuel-use were analysed for diesel bus and hybrid electric bus (HEB) respectively. Finally, the main effects on the fuel consumption were compared between diesel and hybrid buses. 2 Methodology 2.1 Driving pattern parameters The driving data used in this research were collected from the real traffic in two cities. For the traditional diesel bus, 5 buses operation in Nanjing city were collected within 5 days, and 1 day driving for HEB in Hengyang. Not only speed and fuel-consumption were included in the data, but also the longitude and latitude, which provide more information for the research. Table 1 driving pattern parameters Driving pattern parameters Denotation Average speed v m Average driving speed v mr Maximum speed v max Average acceleration a m Average deceleration d m Average time of microtrip c Average length of microtrip l Acceleration root mean square RMS % of idling time p i % of accelerating time p a % of decelerating time p d % of constant-speed time p c % of time in speed interval p 0-20 0_20 km/h % of time in speed interval p 20-40 20_40 km/h % of time in speed interval p 40-60 40_60 km/h The data were all from buses driving, which had fixed routes every day. According to the specificity of buses, the data were divided into many microtrips, depending on the longitude and latitude. In every micro-trip, the buses were driven from original to destination and stayed for over 5 minutes. For each micro-trip, 15 driving pattern parameters were calculated which were shown in Table 1. Each of the driving pattern parameters contained some information about traffic or vehicles. And each reflected different aspects of traffic. The difference between average speed and average driving speed is the former containing the idle time. 2.2 The regression analysis To investigate which driving pattern parameters had important influences on fuel consumption, a regression analysis was performed, with fuel consumption as dependent variable and 13 driving pattern parameters as independent variables. To avoid high degree of linear dependence within the independent variables, two parameters were excluded, one was the p c, and another was p 40-60. As was known, the percentage sum of idle time, acceleration time, deceleration time and constant speed time was 100%, the time percentage of constant speed was removed in the analysis. For time percentage in different speed intervals, the percentage in speed interval 40_60 accounted for the less percentage compared with other intervals, thus, was removed. The following equation was the model of the regression analysis: y 0 1x1... nxn (1) In which, y: fuel consumption as dependent variable x... 1 x n : 13 driving pattern parameters as independent variables. 0, 1... n : unknown parameters. : random error, following Gaussian distributions, and should be independent characters. The n above is 13. During the regression, the number of measurements (N) should be larger than the number of unknown parameters, n. That was also one of the reasons why the data was divided into many micro-trips. The larger N is, the better accuracy is. The regression analysis was performed using the software SPSS. In the results of regression analysis, some indexes could be obtained, which could tell the accuracy of the model and also if the model is EVS28 International Electric Vehicle Symposium and Exhibition 2

significant. One is R 2, representing the accuracy, and the other one is sig, for the significance of the model. 3 Driving pattern parameters in two cities Different driving pattern parameters meant the different traffic traditions between the two cities and also the different performance between the two vehicles. Because of the different traffic conditions and technologies of the buses, fuel consumption in these cities was different. The average fuel consumption of the 5 conventional diesel buses was 42.42L per 100 kilometres and the HEB was 32.36L per 100 kilometres. The results showed that the traditional bus used almost 32.4% more fuel than HEB. Figure 1 showed the speed statistics in Nanjing for traditional diesel bus and Hengyang for HEB. As we can see from Figure 1, the average speed and average driving speed were almost the same in these two cities. However, maximum speed in Nanjing and Hengyang is 52km/h and 35km/h respectively, which meant the higher maximum speed in Nanjing compared with that in Hengyang. This may be due to the better road condition in Nanjing. As showed in Figure 2, in both cities, average deceleration was larger than average acceleration. The reason was about the specificity driving behaviour of city transit bus. While starting vehicles, drivers should be careful of people around, so the acceleration is smaller. While parking, drivers should get to the station more accurately, the heavy brake usually is needed and result in the larger deceleration. Average acceleration and average deceleration in Hengyang were both higher than Nanjing. This reflected the characteristic of hybrid electric bus. The electric motor and generator assisted the start and brake for the HEB causing the large accelerating and decelerating speed. Figure 3 showed the time percentage in different states for buses. Idling time in Nanjing, which was of great importance, made up a high proportion. This accounted for the high fuel consumption of the traditional vehicle. In Hengyang, idling time was much lower, which meant better traffic condition. As for the percentage of accelerating time and decelerating time, in both cities, accelerating time made up a higher proportion. Combined with the result showed in Figure 2, these meant better driving behaviour in Hengyang. Figure 1 Speed statistics in Nanjing and Hengyang The next driving pattern parameters were acceleration and declaration, which represented the variation of the speed. Figure 3 percentage of time in different states Speed intervals were also of great importance for fuel consumption, especially for traditional vehicles. That was because of the influence of working conditions for the engine was significant. Working in a low speed interval meant a poor efficiency, and caused higher fuel consumption. While working in a low speed interval, HEV could use the motor instead of the engine. Its sensitivity of efficiency to speed intervals was much lower. Motors could maintain a high efficiency no matter which speed interval they were in. At the same time, because of the importance of the low speed intervals and the proportion, during the regression analysis, the percentage if time in speed interval 40_60 km/h was removed. For buses in cities, because of the limitation of speed and the specialty of frequent starting and Figure 2 acceleration and declaration in Nanjing and Hengyang EVS28 International Electric Vehicle Symposium and Exhibition 3

stopping, the percentage of time in speed interval 0_20km/h made up a high proportion. As shown in Figure 4, the time percentage in speed interval 0_20km/h in both cities was much higher, Figure 4 percentage of time in different speed intervals especially in Nanjing. The much higher percentage meant the worse traffic and worse fuel consumption, particularly for conventional vehicles. As was shown above, the traffic condition for Nanjing was totally worse than Hengyang, which was largely caused by the population of the cities, the population of vehicles or some other influences. At the same time, traffic condition was an important influence on fuel consumption for vehicles. Therefore the necessary for Nanjing to use electric bus (hybrid or battery) instead of traditional vehicles was significant. 4 Effect of driving pattern parameters on fuel economy 4.1 Results of the regression analysis A model for the prediction of fuel consumption was derived from regression analysis. As described in Table 2, the result of diesel bus was better than HEB. All of the results were acceptable. For the values of sig., both were less than 0.05. This meant the model for HEV and diesel bus was effective. With regard to R 2, both of the two values were larger than 0.6, which meant the prediction model was acceptable. So, the method of regression analysis for fuel-use prediction with driving parameters was used in this section. Table 2 results of regression analysis Diesel bus HEB Sig. 0.000 0.005 R 2 0.746 0.634 4.2 Driving pattern parameters effect on diesel bus As described in Table 3, driving pattern parameters were ordered by their effects on fuel consumption for diesel bus. The effects were graded according to the absolute values of standardised β in the regression analysis. There were four grades here. Standardised β gave the size of the effect (in standards), if the driving pattern parameter changed by one standards unit. Table 3 Driving pattern parameters effects on fuel consumption for diesel bus Driving pattern parameters Effects % of idling time ++++ average speed ++++ % of time in speed interval 0_20 km/h ++++ average acceleration +++ acceleration root mean square +++ average driving speed ++ % of accelerating time ++ average lengths of micro-trips ++ average deceleration + average time of micro-trips + maximum speed + % of decelerating time + % of time in speed interval 20_40 km/h + In Table 3, it was found that, the driving pattern parameters: the percentage of idling time, average speed and the percentage of time in speed interval 0_20 km/h were of great importance for diesel bus. The results showed that idling was one of the most effective influences for diesel bus. While idling, the engine was still working, meaning the waste of fuel-use. While working in a low speed interval, engine had poor efficiency, causing higher fuel consumption. Average speed meant the energy a vehicle needs. So v m was also influential. The driving pattern factor of acceleration was also of great importance for fuel consumption. The results were similar to earlier study [6]. From table 3, we could find other more information. The parameters about micro-trips were less important. Percentage of time in speed interval 20_40 km/h, percentage of accelerating time were more influential than decelerating time. The values of accelerating and decelerating were more important than the percentages. EVS28 International Electric Vehicle Symposium and Exhibition 4

4.3 Driving pattern parameters effect on HEB For HEB, because of the difference of the controlling and working, the regression result was different. As shown in Table 4, the factor of speed was of great influence for HEB. Average speed, maximum speed and % of accelerating time were influential elements. Table 4: Driving pattern parameters effect on HEB Driving pattern parameters Effects average speed ++++ maximum speed ++++ % of accelerating time ++++ average acceleration +++ acceleration root mean square +++ average driving time +++ % of decelerating time +++ average time of microtrips ++ average lengths of microtrips ++ % of idling time ++ time % in speed interval 20_40 km/h + time % in speed interval 0_20 km/h + average deceleration + Table 4 showed that the energy HEV needs was most important. This also meant the efficacy for HEB was much higher. The driving pattern factor of acceleration was also of great importance for HEB which was almost the same as diesel bus. Percentages of time in different speed intervals were less important. While the percentages of accelerating and decelerating were much more influential. The values of accelerating and decelerating were less influential than the percentages of accelerating and decelerating. Contrary to diesel bus, it was shown that the percentage of idling time was at the end, which meant the least influential. 4.4 Comparison of driving parameters effect on diesel bus and HEB As described above, effects of driving pattern parameters on fuel consumption for diesel bus and HEB were different. First of all, the importance of percentage of idling time was different. For diesel bus, while idling, engine was still working, causing the waste of fuel use. But for HEV, because of the controlling, the engine usually be switched off while idling, so the influence was much less. The reason for percentage of time in speed interval 0_20km/h was the same. As for the driving pattern factor of acceleration, the influence was almost the same. While for deceleration, effects for diesel bus were more than HEV. This was because batteries and motor in HEV could recover the energy during the time of decelerating, which resulted in less fuel consumption for HEB. 5 Conclusions (1) The fuel consumption of diesel bus was 32.4% higher than that of hybrid electric bus in two cities. (2) The main driving parameters were largely different for diesel and hybrid bus operation. (3) The fuel consumption was mainly effected by the proportions of different speed intervals and the percentage of idling time for diesel bus, and mainly by the average speed and average acceleration for hybrid bus. (4) The regression equations with acceptable accuracy showed its potential to predict the fuel consumption while the driving pattern parameters were estimated in different real world traffic conditions both for diesel and hybrid electric buses Acknowledgments This work was supported by Ministry of Science and Technology of China, under Contract Nos. 2011DFA60650, 2012DFA81190, 2014DFG71590, 2013BAG06B02, 2013BAG06B04. The authors thank Mr. Guangdi TANG and Mr. Ling Liu from CSR, for providing the driving data of HEB. References: [1]E. Ericsson, "Driving pattern in urban areasdescriptive analysis and initial prediction model," Bulletin 185/3000, 2000-01-01 2000. [2]E. Ericsson, "Independent driving pattern factors and their influence on fuel-use and exhaust emission factors," Transportation Research Part D: Transport and Environment, vol. 6, pp. 325-345, 2001-01-01 2001. [3]H. C. Frey, N. M. Rouphail, H. Zhai, T. L. Farias, and G. A. Gonçalves, "Comparing real-world fuel consumption for diesel-and hydrogen-fueled transit buses and implication for emissions," Transportation Research Part D: Transport and Environment, vol. 12, pp. 281-291, 2007-01-01 2007. [4]H. Wang, L. Fu, Y. Zhou, and H. Li, "Modelling of the fuel consumption for passenger cars regarding driving characteristics," Transportation Research EVS28 International Electric Vehicle Symposium and Exhibition 5

Part D: Transport and Environment, vol. 13, pp. 479-482, 2008-01-01 2008. [5]M. André and M. Rapone, "Analysis and modelling of the pollutant emissions from European cars regarding the driving characteristics and test cycles," Atmospheric Environment, vol. 43, pp. 986-995, 2009-01-01 2009. [6]E. Ericsson, "The relation between vehicular fuel consumption and exhaust emission and the characteristics of driving patterns,", 2000, pp. 137-147. Authors Ming CHI received her B.E. Degree in automotive engineering from Tsinghua University, Beijing, China. She is now pursuing M.E. Degree in automotive engineering in Tsinghua University, China. Her research interests include driving cycle and driving pattern. Hewu WANG is the Associate Professor and Deputy Director of the China-US Clean Vehicle Consortium from Tsinghua University, China. His research interests include the alternative fuels, industrialization and demonstrations of electric vehicle. Minggao OUYANG, is the Professor from Tsinghua University, China. He is Directing the State Key Laboratory of the Automotive Safety and Energy, Tsinghua University, China. EVS28 International Electric Vehicle Symposium and Exhibition 6