| Citation: | WU Sheng-li, XING Wen-ting, SHAO Yi-ming, JIAN Xiao-chun, ZHAO Shu-en. Analysis of factors affecting vehicle driving condition based on road test in Chongqing[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 150-158. doi: 10.19818/j.cnki.1671-1637.2021.02.013
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The fuel economy and emission characteristics based on actual road driving conditions are receiving increasing attention: Jiang et al[4]Constructed actual road driving conditions using wavelet analysis method; Guo Jiachen and others[5]To optimize the driving performance of automobiles, a vehicle kinematic segment feature system was established based on 9 representative characteristic parameters closely related to driving characteristics, using measured data from light vehicle roads as the data source. A method for constructing urban road vehicle driving conditions was proposed; Zhan Liangliang, etc[6]Adding high cetane value hydrogenated catalytic biodiesel to 95 octane gasoline in different volume ratios increases particulate matter emissions as the volume fraction of biodiesel increases, but can effectively reduce CO and unburned hydrocarbon emissions; Zhao Yuwei and others[7]The influence of blending a new alternative fuel, high oxygen content and high cetane number, poly (methoxy dimethyl ether), into diesel fuel was studied on the combustion and emission characteristics of Chinese Stage VI standard diesel engines, NOxThe emissions have slightly increased, but the emissions of CO and HC will significantly decrease; Adams and others[8]The study investigated the effects of adding 5% and 10% biodiesel to gasoline on its compression ignition and emission characteristics. The results showed that this method not only effectively reduced the intake temperature requirements of pure gasoline, but also increased combustion stability; Putrasari et al[9]Similar methods were used to conduct relevant research, and the results showed that the thermal efficiency of blended fuel was comparable to diesel, and HC emissions were significantly reduced compared to diesel. However, NOxThere will be a significant increase in emissions; Dong Hongzhao and others[10]Based on the revised IVE model and combined with actual driving conditions, a comprehensive emission factor was proposed to characterize the emission characteristics of light vehicles and taxis. At present, Vehicle Specific Power (VSP) is considered an effective indicator for comprehensive evaluation of emission characteristics[11]Not only is it widely used in traditional fuel vehicles, but it has also been applied in the emission characteristics of hybrid vehicles[12-14]Zhang et al[15]The portable emission measurement system (PEMS) was used to study the gas pollutant emission data of light-duty gasoline vehicles with different emission standards during mountain and road driving. The emissions of pollutants from vehicles with newer emission standards were significantly lower, and the differences were more pronounced during mountain driving; Chong et al[16-17]Firstly, real-time emission measurement data was used to characterize the gas emission behavior of diesel vehicles while driving on the road using multiple linear regression. Then, the variation of vehicle pollutant emissions under different road and working conditions was studied, and it was found that diesel vehicles had higher pollutant emissions in the low-speed driving range; Luj á n et al[18]Actual tests were conducted on the gas emissions of Euro 6 light-duty diesel vehicles on the Valencia route in Spain, and the calculation methods proposed by the actual driving emission regulations were analyzed for gas emission rates and NOxThe impact of emission compliance factors, while pointing out that lower speeds, more acceleration and deceleration will produce higher levels of NO than constant high speedsxEmissions; Yang et al[19]PEMS was used to conduct actual tests on the emissions of one gasoline and two diesel light-duty passenger vehicles with Euro 6b standards in Lyon, France. The changes in instantaneous emission rates under different road types and operating conditions were analyzed.
Based on actual road driving conditions, this study investigates the fuel consumption rates of different vehicle models under different driving speeds[20-22]Zhang Jinhui and others[23]Proposed an estimation model for transient fuel consumption using the least squares method, and achieved estimation of fuel consumption; Stillwater and others[24]We studied the effects of driving feedback, driving behavior, and other conditions on the fuel economy of automobiles, and found that driving behavior influenced by energy feedback can reduce overall fuel consumption by 4.4%; Bernardo et al[25]The influence of engine oil viscosity on the fuel economy of medium-sized diesel engine trucks was studied. Higher viscosity engine oil increased fuel consumption during lubrication, while lower viscosity engine oil reduced fuel consumption; Pitanuwat et al[26]Combining road tests in Bangkok, the impact of traffic conditions and driving styles on fuel economy was analyzed, and it was pointed out that hybrid vehicles can reduce fuel consumption under different traffic conditions and driving styles, especially on urban roads, where fuel consumption is reduced by nearly 56%; Zahabi et al[27]We studied the fuel economy of hybrid vehicles on different road sections in low-temperature environments and found that the fuel efficiency of hybrid vehicles in winter decreased by about 20% compared to summer; Ma et al[28]We studied the impact of driving behavior on fuel economy and found that the fuel consumption during acceleration accounts for a large proportion of the total fuel consumption. Moreover, the average depth of the accelerator pedal and the timing of gear shifts during acceleration have a significantly higher impact on fuel consumption than during normal driving.
Current research focuses more on the construction of actual operating conditions, fuel consumption models, and qualitative analysis of the impact of emission characteristics under different operating conditions, lacking quantitative analysis of the impact of driving conditions on fuel economy and emission characteristics. This article quantitatively analyzes the impact of different working conditions on vehicle fuel economy and emission characteristics through actual road tests and the comprehensive use of projection pursuit dynamic clustering method, providing practical support for optimizing traffic engineering design and intelligent vehicle control.
Using Sensors' Semtech Ecostar vehicle exhaust collection system, VBOX device, and LPMS-CU gyroscope to obtain vehicle emission parameters, vehicle driving speed, vehicle acceleration, vehicle angular velocity, vehicle angular acceleration, and azimuth angle, with a sampling frequency set to 1 HzFigure 1As shown.
Conduct road tests on the sections of Nan'an District and Yuzhong District in Chongqing, with specific driving routes as followsFigure 2As shown. This section includes typical sections such as the Caiyuanba Yangtze River Bridge and the Yangtze River Expressway, with a speed limit of 80 km/h on Haixia Road-1The route has formed working conditions such as fast driving sections, normal driving sections, congested and slow driving sections, and the entire section has various road forms such as flat, uphill and downhill, sharp turns, and overpasses. Throughout the entire test process, the vehicle drove normally within the allowed speed range, without frequently shifting gears or changing lanes to overtake.
Study the fuel economy and emission characteristics of vehicles using vehicle speed, acceleration, turning radius, and road slope as parameter indicators.
| xij=xjmax−x0ijxjmax−xjmin | (1) |
| xij=x0ij−xjminxjmax−xjmin | (2) |
| zi=em∑j=1ajxij−1em∑j=1ajxij+1 | (3) |
In the formula:ajThe weights of different evaluation indicators.
In the process of dynamic clustering, the first step is to extract the feature valuesziThe assumed evaluation level isp， Θt(t=1, 2, …, p, p≤n）For the thtThe set of projected feature values for class samples can be represented as[31]
| Θt={zi∣d(At−zi)⩽ | (4) |
| \psi_{\mathrm{a}}=\sum\limits_{i=1}^{p}\left[\max \left(\Theta_{t}\right)-\min \left(\Theta_{t}\right)\right] | (5) |
usesaIndicates the degree of aggregation between samples, with larger values indicating higher dispersion between samples; Θl⊂Θt、 Θk⊂Θtjustl≠k，saExpressed As[31]
| s_{\mathrm{a}}=\sum\limits_{l, k \in n, l \neq k}\left|\Theta_{l}-\Theta_{k}\right| | (6) |
Obtain dynamic clustering indicators for curve projection trackingQa=sa－ψa，QaThe larger the sample size, the more clustered the samples within the class and the more dispersed the samples between classes; approachQaWhen taking the maximum value, the optimal clustering result and the optimal projection direction vector that best reflect the characteristics of the data can be obtained.
| \begin{aligned} &\max Q_{\mathrm{a}}=s_{\mathrm{a}}-\psi_{\mathrm{a}} \\ &\text { s. t. } \sum\limits_{j=1}^{m} a_{j}^{2}=1 \\ &-1 \leqslant a_{j} \leqslant 1 \end{aligned} | (7) |
The maximum allowable speed on some roads is 80 km/h-1During actual driving, the maximum speed of the vehicle was measured to be 62 km · h-1According to the engine characteristic curve, the driving speed of the entire road section is basically within the range where the higher the speed, the better the fuel economy. According to automotive theory, the impact of acceleration, turning radius, and slope on fuel economy is known. The level of vehicle exhaust emissions is closely related to the output power of motor vehicles, therefore, adoptingVVSPTo measure the level of motor vehicle emissions,VVSPThe calculation formula can be expressed as[12]
| \begin{gathered} V_{\mathrm{VSP}}=v[\delta(1+\varepsilon)+g(\beta+f)]+ \\ \frac{\rho_{\mathrm{a}} C_{\mathrm{D}} B v}{2 M}\left(v+v_{\mathrm{m}}\right)^{2}+C_{\mathrm{f}} g v \end{gathered} | (8) |
In the formula:MFor the quality of automobiles;vFor the speed of the car;δAcceleration of the car;εFor quality factor;gAcceleration due to gravity;βFor slope;fFor rolling resistance coefficient;CDIs the coefficient of air resistance;BFor the windward area;ρaFor air density;vmFor the windward speed of the car;CfThe coefficient of internal friction resistance.
The collected data on turning radius, acceleration, speed, and slope were processed according to the calculation process, and the weights of their impact on vehicle fuel economy were obtained as 21.54%, 65.52%, 11.70%, and 1.24%, respectively; The weights that affect the VSP characteristics of vehicles are 37.86%, 35.03%, 9.18%, and 17.93%, respectivelyFigure 5As shown. Insufficient fuel combustion during acceleration can increase fuel consumption and worsen emission characteristics. In addition, Chongqing is a typical mountainous city where roads are mostly built along the mountains, resulting in many bends and varying slopes. This not only increases the impact of vehicle turning radius, but also increases the weight of slope influence.
The measured data of turning radius, acceleration, and slope at different speeds are as follows:Figure 6As shown. When the speed is low, the slope of the road changes more significantly.
The impact of different speed ranges on vehicle fuel economy and VSP characteristics is as follows:Figure 7 (a)、8(a)As shown; The comprehensive cumulative impact is as followsFigure 7 (b)、8(b)As shown. causeFigure 7、8Visible: speed less than 10 km · h-1The weights of the impact of turning radius, acceleration, speed, and slope on vehicle fuel economy are 80.74%, 9.99%, 9.21%, and 0.06%, respectively; The weights of the impact on vehicle VSP characteristics are 0.01%, 82.82%, 17.15%, and 0.02%, respectively; The speed is 10-40 km · h-1At that time, the weights of each indicator parameter on the fuel economy of vehicles were 20.09%, 34.01%, 21.82%, and 24.07%, respectively; The weights of the impact on vehicle VSP characteristics are 2.08%, 48.59%, 12.09%, and 37.24%, respectively; Speed greater than 40 km/h-1At that time, the weights of each indicator parameter on the fuel economy of vehicles were 15.46%, 1.12%, 7.83%, and 75.59%, respectively; The weights of the impact on vehicle VSP characteristics are 0.59%, 0.23%, 80.17%, and 19.01%, respectively.
Speed less than 10 km/h-1When driving at low speeds, turning the vehicle will reduce the effective driving force, making the turning radius have a significant impact on the fuel economy of the vehicle; At this point, frequently pressing the accelerator pedal not only causes a significant change in vehicle acceleration, but also exacerbates the deterioration of VSP characteristics. Vehicle speed is 10-40 km · h-1Acceleration becomes the main factor affecting fuel consumption and VSP characteristics, while the influence of slope is also significant. Speed greater than 40 km/h-1The impact of slope on fuel economy increases sharply, and changes in speed also have a significant impact on VSP characteristics.
(2) During the entire driving process, the turning radius and acceleration have a significant impact on the fuel economy and VSP characteristics of the vehicle. Within a specific speed range: when the speed is less than 10 km · h-1When turning, the turning radius has a significant impact on the fuel economy of the vehicle, while the acceleration of the vehicle has a significant impact on the VSP characteristics; The speed is 10-40 km · h-1Acceleration is the main factor affecting fuel economy and VSP characteristics; Speed greater than 40 km/h-1The impact of slope on fuel economy increases sharply, and changes in speed also have a significant impact on VSP characteristics. Within a specific slope range: on uphill and downhill sections, vehicle acceleration and slope have a significant impact on vehicle fuel economy, respectively. On flat sections, vehicle acceleration and speed have the most significant impact on vehicle overall performance.
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WU Sheng-li, XING Wen-ting, SHAO Yi-ming, JIAN Xiao-chun, ZHAO Shu-en. Analysis of factors affecting vehicle driving condition based on road test in Chongqing[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 150-158. doi: 10.19818/j.cnki.1671-1637.2021.02.013
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