Development of Hybrid City Bus’s Driving Cycle Shiqi Ou, Yafu Zhou
Jing Lian*, Pu Jia, Baoyu Tian
School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment
School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian, 116024, P R China
Dalian University of Technology Dalian, 116024, P R China
[email protected] Abstract—We in this study adopted “Electric Vehicle Information Terminals and Remote Monitoring System” which is researched and developed independently to collect vehicle’s data. We mainly used MATLAB software to statistically analyze the monitored hybrid buses’ actual driving data on the road with micro-trip analysis method, and constructed the comprehensive driving cycle of hybrid bus for this city. The driving cycle construction method and procedure were further perfected in this study. Then the driving cycle was simulate calculated and compared with road driving conditions in the engine and motor’s operation performance and fuel consumption in order to verify the accuracy of the constructed driving cycle and widely applicability of the construction method. The driving cycle and relevant results can directly affect the selection of hybrid electric vehicles (HEVs) components and the control strategy, and be the basis of testing the hybrid electric vehicle’s driving system and motor system optimization matching, furthermore provide evaluation standard for the hybrid city bus’s fuel consumption. Keywords-data collection; hybrid city bus; driving cycle; simulated cacullation
I.
INTRODUCTION
Vehicle’s driving cycle reflects the typical driving dynamic conditions of a vehicle in a specific region, nowadays there are some standard driving cycles home and abroad, the standard driving cycles abroad such as the United States FTP75, European ECE15, Japan 10115 etc, the domestic standard driving cycles such as CTBDS_UD for energy consumption test of heavy-duty hybrid vehicle. These driving cycles are widely applied in the area of the formulation of vehicle emission regulations, the design and development of new car model, assessment of pollutant emission and fuel consumption’s measurement in specific areas. At the same time the characteristics of driving cycle indicate that driving cycle has its pertinence as well as relative applicability. The traffic conditions could be different due to the traffic capacity, the road conditions and the regional prosperity. Therefore, perfecting the driving cycle’s construction method and the development of driving cycle for local city have practical meanings and values. The driving cycle for new energy vehicle such as hybrid vehicle can provide a reliable basis for the improvement of motor control strategy and vehicle matching and can be applied to the evaluation of the hybrid vehicle's engine economic
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performance and emission performance. The driving cycle constructed in this paper can be used for improvement of HEV’s motor control strategy and be the reliable basis for vehicle matching and evaluation for the HEV performance. Besides, in this paper, the hybrid bus’s comprehensive driving cycle is also simulated and compared thus ensure the driving cycle is practical and reliable, and verify the accuracy and reasonableness of the construction method, the driving cycle’s construction method in this paper can widely be applied to other regions’ driving cycle’s construction. II.
PREPARATION FOR DRIVING CYCLE CONSTRUCTION
The general process of driving cycle construction method includes: test planning, data collection, data analysis, driving cycle construction, driving cycle verification, in which the critical process is the driving cycle construction method. There are different driving cycle construction methods that determine whether the final driving can truly reflect the actual traffic and geographical characteristics of driving conditions. A. Test planning Test planning mainly aimed at the preparation of driving cycle, it included knowing the monitored vehicle’s parameters, the installation of the data acquisition instrumentation, and the test route’s selection, among which the test route’s selection was the most important task which played an important role in driving cycle construction. The test route’s selection was on the basis of the average traffic so to determine test direction and test time, taking into account the difference traffic between holiday and working day. In this study, four hybrid buses were selected to be tested on working days, and the whole test route is around 15.5 km (Road conditions include pathways, detours railroad crossings, overpasses, annular crossroads, bus lanes, tram lanes, along with schools, commercial outlets, entertainment venues, parks, hospitals, kindergartens, residential area). The route basic covers the city's main residential, commercial and industrial areas, the test route’s traffic and the average city traffic are essentially flat. Therefore, the test route is representative for the construction of driving cycle. B. Data collection
In view of the actual situation that the research was mainly directed against the hybrid bus’s driving cycle, so the test method adopted “test vehicle self-driving method”, it is a method that the bus runs in accordance with planned routes independently, which can truly reflect the road conditions of urban public transport system, and help to obtain a large number of test data with sufficient data collection instruments. The "Electric Vehicle Information Terminals and Remote Monitoring System" was adopted in this experiment, the system communicated through GPRS or 3G wireless communication network and remote terminals, it could remote monitor and intelligent fault diagnose the buses’ real-time conditions, simultaneously made a standard evaluation and analysis for the buses’ energy consumption indicators, the running conditions, the dynamic operating parameters of key parts, the motor, battery control system performance. The system not only greatly facilitated the researchers monitor buses’ real-time tracking, but also collected data more accurately and effectively. The testing equipment’s measuring sensor devices were installed on the buses, including engine speed sensor, speed sensor, motor speed sensor, battery voltage and current sensors, memory, clock, ECU, etc. The test data included 26 parameters such as engine’s speed, vehicle speed, motor’s speed. The data signal was transmitted through the GPRS system to the laboratory’s terminal server for monitoring. III.
CONSTRUCTION OF HYBRID BUS’S DRIVING CYCLE
In this study the data was obtained in the form of a database, and this study developed the driving cycle based on MATLAB software to enable the data analysis and driving cycle construction more convenient. The method discussed in this paper was based on the construction of micro-trips’ combination method, and the data building for the driving cycle was on account of the actual running parameters-time data collected after a selection of representative driving route and test hybrid buses. Had obtained the data with the data acquisition system, we firstly divided the data into micro-trips, where we defined the micro-trip is: a running process that vehicles run from one stop to the next stop. In order to adequately describe a micro-trip, this paper defined 15 characteristic values, and the characteristic value is a kind of parameter that can represent the vehicle running features. The characteristic values in this paper included: the running length S, the maximum speed Vmax, the mean speed Vm, the running speed Vr, the standard deviation of speed Vsd, the running time T, the acceleration time Ta, the deceleration time Td, the cruise time TC, the idling time Ti, the maximum acceleration amax, the average acceleration of accelerate segment aa, the minimum acceleration amin, the average deceleration of decelerate segment ad, the standard deviation of acceleration asd. Each characteristic value was calculated in the light of the value which is corresponding to the speed-time in each micro-trip, after that the characteristic values were classified into a database in the MATLAB.
Figure 1. Principal component’s contribution rate diagram
A. Principal Component Analysis According to the characteristic values of each micro-trip, the principal component analysis was used to reduce the dimension of these micro-trips in MATLAB software. The principal component analysis that is constructed a series of linear combinations of the original variables, so that the linear combination in each other irrelevant premise as much as possible reflects the original variables and ignores the largest variance. Generally we selected the linear combination that can reflect more than 80% or 90% original variable information depending to the different needs. During development of hybrid bus’s driving cycle, we in this paper pertinently analyzed the low-speed, medium-speed, high-speed movement patterns, and on this basis, built driving cycle, at the same time built the low-speed, medium-speed, high-speed’s driving cycles. Thus we applied the principal component analysis to classify the micro-trips in accordance with the traffic features that is the characteristic values of the micro-trips (such as the maximum speed and the mean speed, etc.). We principal component analyzed the database of microtrips’ characteristics values and Fig. 1 presents the contribution rate of 15 principal components and their cumulative contribution rate. Shown in Fig. 1, the first few principal components contain most information of the 15 characteristic values been defined in the above, which the first four principal components’ cumulative contribution rate achieves 86.9489%. Therefore, we just choose first four principal components for following calculation and analysis. At the same time the first four principal components’ correlation coefficients of characteristic values are shown in Table I. We analyzed this table and found from the correlation coefficients that: •
Principal component 1 mainly reflects the running length, the maximum speed, the mean speed, the running speed, the standard deviation of speed, the deceleration time, the running time, the cruise time, the minimum acceleration.
TABLE I.
THE CORRELATION COEFFICIENTS OF FIRST 4 PRINCIPAL COMPONENTS AND CHARACTERISTICS VALUE
Value
S
Vmax
Vm
Vmr
Vsd
T
Ta
Td
Tc
Ti
amax
aa
amin
ad
asd
P1
0.855
0.896
0.862
0.838
0.763
0.625
0.681
0.616
0.733
-0.150
0.492
-0.007
-0.602
-0.205
0.472
P2
0.455
-0.243
-0.194
-0.334
-0.441
0.679
0.643
0.709
0.513
0.334
-0.355
-0.608
0.551
0.700
-0.777
P3
0.031
-0.145
-0.335
-0.270
-0.137
0.299
-0.012
0.078
0.199
0.399
0.615
0.611
-0.084
-0.034
0.116
P4
0.079
-0.077
0.156
-0.129
-0.129
-0.197
-0.002
0.142
0.152
-0.789
0.258
0.292
0.250
0.340
0.048
•
Principal component 2 mainly reflects the acceleration time, the average deceleration of decelerate segment, the standard deviation of acceleration.
•
Principal component 4 reflects the idle time. TABLE II.
THREE GROUPS OF DRIVING CYCLES’ CHARACTERISTIC VALUES
Characteristic Value
The 1st Category
The 2nd Category
The 3rd Category
S(m)
583.61.
197.78
343.33
Vmax(km/h)
42
29
38
Vm(km/h)
18.43
5.70
17.66
Vmr(km/h)
22.59
13.96
24.24
Vsd(km/h)
14.61
8.22
14.89
T(s)
114
125
70
Ta(s)
9
11
5
Td(s)
12
9
4
TC(s)
72
31
42
Ti(s)
21
74
19
amax(m/s2)
3.06
3.06
4.72
aa(m/s2)
1.33
1.29
2.22
amin(m/s2)
-3.33
-3.33
-3.61
2
-1.27
-1.70
-2.85
2
asd(m/s )
0.71
0.71
1.00
Pa(%)
7.89
8.80
7.14
Pd(%)
10.53
7.20
5.71
PC(%)
63.16
24.80
60.00
Pi(%)
18.42
59.20
27.14
P0-10(%)
16.67
16.00
15.71
P10-20(%)
23.68
22.40
7.14
P20-30(%)
9.65
2.40
14.29
P30-40(%)
28.07
0
35.71
P40-50(%)
3.51
0
0
ad(m/s )
B. Cluster Analysis Cluster analysis is a kind of statistical method that classifies objects which are corresponding to a class of numerous data. The aim of classifying overall traffic characteristic values groups by cluster analysis is to sort out the similar characteristic values groups into one category. Then we analyzed the groups which were on behalf of different types of roads, and parsed out the appropriate type of driving cycles. According to the regional features of the city, the traffic conditions of a variety of different levels can be artificially divided into different driving cycles, and the cluster analysis can fragment the data into different categories in the light of the micro-trips’ kinematics rules. Based on the urban road traffic conditions we determined three categories of driving cycles, these three categories were respectively parallel with low-speed( high idle time proportion and low average speed), medium-speed( normal acceleration and deceleration and moderate average speed), high-speed( running continuously and high average speed). We formed three micro-trip’s databases after dividing overall micro-trips into three categories with cluster analysis. The micro-trips’ scores mainly reflect the classification and mirror the traffic capacity and the traffic smooth degree. All the micro-trips were divided into three categories of driving cycle databases. Table II shows the three categories of comprehensive descriptive characteristic values of micro-trips. The correlation coefficient was applied to analyze the micro-trips. The closer one micro-trip’s correlation coefficient is to 1, the better that micro-trip’s characteristic values and overall micro-trips’ characteristic values’ linear correlation are, and that micro-trip’s data is more exact. Thus we can select the most representative micro-trip, as Fig. 2 shows. C. Construction of Comprehensive Driving Cycle We established the comprehensive driving cycle through the above three categories driving cycles. A typical driving cycle’s time’s length generally is 900s-1400s. Here is the way to find out how many micro-trips contained in certain category driving cycle in the process of driving cycle construction: the expected time spent by the certain category driving cycle in the driving cycle construction divides the average operating time of this category, then round the number to the nearest integer. Fig. 3 shows the comprehensive driving cycle. The time of the comprehensive driving cycle is 1222s, length is 5.79km, fuel consumption per hundred kilometers is
Figure 3.
Hybrid city bus driving cycle diagram
Fig.6 shows driving cycle simulation’s and actual hybrid bus’s motor speed-torque. The analysis indicates that the motor working points intensive focus on speed 1500-2800rpm, torque 0-75%. Motor’s efficiency is about 70%, and the efficiency first increases and then decreases. Compared with the two graphs, the image distribution of simulated speed-torque graph is smoother, and motor is more efficient, this is because the motor control is much reasonable in the case of constructed driving cycle, appropriate motor control strategies play a very important role on hybrid bus’s energy recovery.
Figure 2. Three categories driving cycles diagram
31.94L/(100km), the maximum speed is 42km/h, the average speed is 17.07km/h, the maximum acceleration is 4.72m/s2, the maximum deceleration is -5.00m/s2. Gear transform is dominated by 2th gear, accounting for 34.45%. Fig. 4 shows the gear distribution of the comprehensive driving cycle. IV.
SIMULATION AND COMPARATIVE ANALYSIS OF HYBRID BUS’S DRIVING CYCLE
B. Comparison and Verification for Driving Cycle’s Fuel Consumption The hybrid bus’s fuel injection volume was monitored at the same time that the “Electric Vehicle Information Terminals and Remote Monitoring System” collected data for the test. Based on the Fuel injection volume data, we computed the hybrid bus’s average fuel consumption per hundred kilometers is 33.27L / (100km) under the same conditions in the same period, in the meantime, the driving cycle’s fuel consumption per hundred kilometers is 31.96L / (100km), and the percentage error is less than 3.9%, which verifies the constructed comprehensive driving cycle and hybrid bus’s actual driving conditions are higher similarity, and the constructed comprehensive driving cycle in this paper can truly reflect the city hybrid bus’s running conditions.
A. Simulation and Comparison of Engine and Motor With MATLAB software we simulated that the hybrid bus runs under the condition of driving cycle constructed in the above, and we obtained the simulate data. We comparative analyzed the data obtained from the simulation test with the data collected from the real driving conditions, so as to verify whether the comprehensive driving cycle in this paper can be applied to the practical conditions. Fig.5 shows the comparison of engine torque - speed graph for the driving cycle. From Fig.5 we can find that the simulated engine working conditions of constructed driving cycle and the actual engine working conditions are very close to each other, meanwhile the simulated engine working condition’s distribution of points is smooth and reasonable and in line with practical conditions.
Figure 4. Gear distribution of the driving cycle
Figure 6. Motor torque-speed comparison
Figure 5. Comparison of engine torque-speed
C. Analysis of the comprehensive driving cycle Cruise speed time constitutes the main part of hybrid bus’s driving process, and the cruise speed time’s proportion exceeds 50%. Meanwhile idle time takes a high proportion 25.86%, so the research on energy saving at idle time can be regarded as a breakthrough in hybrid bus’s fuel-efficiency. Also the proportions of acceleration and deceleration are lower while the bus seldom stops or starts frequently, it indicates that the road conditions of test route for hybrid bus is commendable; yet average speed is only 17.07km/h, which is related to the test route’s topography, too many slopes and curves restrict the bus's speed.
MATLAB. Through the analysis and results of simulation and comparison, it verified that the construction method in this paper has its practical value and can be widely applied in other conditions, besides, the comprehensive driving cycle is pragmatic for this city, and the driving cycle can contribute to further study the motor’s control strategy of hybrid bus and provide a reliable practical foundation for detection of fuel consumption. REFERENCES [1]
[2]
[3]
V.
CONCLUSION
We adopted self-developed “Electric Vehicle Information Terminals and Remote Monitoring System” to collect test hybrid buses’ data, and constructed the hybrid city bus driving cycle through micro-trip analysis method. The construction of the hybrid city bus driving cycle was verified it’s the reliability and practical applicability by simulation and comparison with
[4]
[5]
Sun Hongtu, Song Xigen, Wang Tianling, Improved method of driving cycle construction for urban bus. Journal of Dalian University of Technology, Vol 49, pp.837-841, 2009 (In Chinese). Li Mengliang, Zhu Xichan, Zhang Jianwwi, Zhang Fuxing, Ai Guohe, A study on the construction of driving cycle for typical cities in China. Automotive Engineering, Vol 27, pp.557-560, 2005 (In Chinese). Liu Minghu, Zhao Ziliang, Li Jun, Wang Yuncheng, Wang Jianhua, A study on the development of driving cycle for public buses in Beijing. Automotive Engineering, Vol 27, pp:687-690, 2005 (In Chinese). W.T. Hung, H.Y. Tong, C.P. Lee, K. Ha, L.Y. Pao. Development of a practical driving cycle construction methodology: A case study in Hong Kong. Transportation Research Part D, Vol 12, pp:115-128, 2007. Wang Qidong, He Kebin,Yao Zhiliang, Huo Hong, Research in motor vehicle driving cycle of Chinese citie. Environmental Pollution and Control, Vol 29, pp.745-748, 784, Fall 2007 (In Chinene).
