- Original Article
- Open Access
Car Fuel Economy Simulation Forecast Method Based on CVT Efficiencies Measured from Bench Test
© The Author(s) 2018
- Received: 29 April 2016
- Accepted: 21 September 2018
- Published: 4 October 2018
Researchers face difficulties in studying the effects of driveline efficiency on car fuel economy via bench and road tests because of long working periods, high costs, and heavy workloads. To simplify the study process and shorten test cycles, a car fuel economy simulation forecast method for combining computer simulation forecasting with bench tests is proposed. Taking a continuously variable transmission (CVT) as the research object, a transmission efficiency model based on a bench test is constructed. An optimal economic variogram based on the original CVT variogram, the boundary conditions of vehicle performance, the road conditions and the driving behavior of the driver is generated in the Gear Shift Program (GSP)-Generation module in AVL Cruise. And on this basis a driveline simulation model that can calculate the fuel consumption based on the driveline data of a test car is built. The model is used to forecast fuel consumption and calculate real-time CVT efficiency under different conditions. Contrastive analyses on simulation results and real car drum test results are made. The largest error between simulation results and drum test results in driving cycles is 4.099%, which is 5.449% under constant velocity condition in driver control mode and 4.2% under constant velocity condition in automatic cruise mode. The results confirm the feasibility of the method and the good performance of the driveline simulation model in accurately forecasting fuel consumption. The method can efficiently investigate the effects of driveline efficiency on car fuel economy. Moreover, this research provides instruction for accurately forecasting fuel economy as well as references for studies on the effects of drivelines on car fuel economy.
- Fuel economy
- CVT efficiency
- Simulation forecast
Driveline efficiency can significantly affect car fuel economy, and studying this relationship through bench and road tests requires long working periods, high costs, and heavy workloads. Such tests can also lead to certain blindness for the determination of vehicle and various assembly schemes, the selection of structural parameters and the matching of driveline parameters with an engine, all of which can affect the creation of a better matching program, result in an unsatisfactory product performance, and wastage of resources . Therefore, various computer simulation forecasting methods assisted by bench tests have been widely adopted to optimize driveline matching and study transmission efficiency. Such methods can improve design quality, shorten the development cycle, and reduce costs . Numerous studies on the efficiency of CVT and gearbox have been conducted through bench tests in China and abroad. Akehurst et al. [3–5] used bench tests to study torque losses in a metal belt CVT, which were caused by the relative sliding motion between belt segments and bands. They then forecasted and studied torque losses between belt segments and bands caused by the bending and deformation of the pulley based on experimental observations of pulley deflections. Furthermore, they studied the sliding speed of belt segments relative to the primary and secondary pulleys and its effects on torque loss, after which they proposed a model for the effects. They validated their proposed model using a number of experimental data. Meanwhile, various transmission power losses of CVT have been analyzed, and the transmission efficiencies of metal belts have been measured through bench tests . However, the effects of CVT efficiency on car fuel economy have not yet been studied through real car tests. Huang et al.  conducted a CVT efficiency bench test using an electronically controlled gasoline engine as the power source, in which ratio, torque, and torque transmission ranges with high efficiency were measured. However, they did not validate the results using a real car test. In addition, researchers have investigated the influences of gear precision, bearing fit quality, and gear oil in the gearbox on transmission efficiency. Wang et al.  selected manual transmission as the research object to determine the influence of different gear precisions on transmission drive efficiency under various working conditions through bench tests. The results showed that a high-precision gear with a grinding process could evidently improve transmission drive efficiency. Gear precision mainly influences mesh precision and transmission dynamic stability. Considerable work has been done to analyze the influence of different types of gear oil on transmission efficiency under various working conditions through bench tests [9–11]. Similarly, Zhu et al.  studied the power loss of CVTs, including torque loss and speed loss, based on the transmission mechanism of CVTs, and experiments on CVT efficiency were conducted on a specific CVT test bench. Furthermore, they provided optimal structural parameters to serve as references for designing and tuning CVTs.
Numerous studies on CVT efficiency have been conducted using computer simulation forecasting. Luo et al.  developed an optimum economic control strategy considering both engine and CVT efficiencies to compensate for the deficiencies of traditional optimum economic matching strategies, and this method was validated through simulations and bench tests. Cai  built one loss model for the hydraulic pump and the metal belt and another model for CVT pressure and ratio based on experimental data. In addition, driving cycle simulations based on these models and real car tests have been conducted to validate the models and analyze the effects of hydraulic pump and metal belt losses. Wang  developed efficiency models of driveline components based on the analytical method. Afterward, contrastive simulations of driving cycles and constant velocity conditions based on fixed transmission efficiencies and efficiency models was conducted. The simulation results showed that the simulation of fuel consumption based on the efficiency models was close to the actual results, which were measured in real car tests. However, difficulties in high-precision analytical models and a substantial number of calculations have been encountered in using these efficiency models. Yan et al.  studied the sensitivity of the effect of driveline efficiency on the car fuel economy of a step transmission car through fuel economy simulation programs. Huang  proposed a forecast program based on the establishment of engine universal characteristic forecast models and the calculation methods for fuel economy, this program was subsequently validated via real bus tests. A fuel economy forecast method was also proposed based on the test procedure established by the United States Federal Environmental Protection Agency; this method was validated through simulation analyses .
To facilitate data development and reference matching, simplify calculation models, and ensure the accuracy of simulation models without performing a real car test, the transmission efficiency data of various components and losses of driveline measured through bench tests are used as assistant data and inputted into computer simulation software to forecast car fuel economy. In this research, a method for combining computer simulation forecast with CVT efficiency bench test is proposed based on the study results above. A driveline simulation model based on CVT efficiencies is subsequently built to forecast car fuel consumption. In Section 2, the driveline simulation model considering the fuel consumption calculation and the input interface of the CVT efficiency model is modeled. Then in Section 3, the CVT efficiency is tested through bench tests, and a function of the piecewise programming lookup is proposed, CVT efficiency model based on this function is connected with the input interface of the driveline simulation model. Driveline simulation system structure is presented in Section 4, and the optimal economic variogram assisting in improving torque converter (TC) efficiency and transmission efficiency  is generated to achieve ratio control. Test cycles and conditions are presented in Section 5, the simulation model and the real car drum tests in Section 6 are run to follow the conditions. Real car drum tests, contrastive analysis and discussions of simulation and drum test results are presented in Section 6, followed by conclusions. The results of the contrastive fuel consumption simulations and the real car drum tests show that the car driveline simulation model based on CVT efficiencies measured from a bench test and the driveline data of a test car provides good accuracy in forecasting values, thereby providing effective references for future studies on the effects of driveline on car fuel economy.
The dynamic response process of the engine output torque is not considered when the throttle opening is changed transiently.
The torsional stiffness and viscous damping of the driveline are not considered.
The dynamic characteristic effects of the CVT hydraulic actuators are not considered, and the relationship between CVT ratio and ratio change rate is described using a simple integral. The effects of lockup clutch oil charge and discharge characteristics on the locking and unlocking processes are disregarded. Moreover, the effect of temperature on transmission efficiency is also not considered.
2.1 Engine Modeling
2.2 Car Driveline Modeling
2.2.1 TC Equipped with a Lockup Clutch Modeling
In practical calculation, the slipping of TCC is not considered, and TCC is directly controlled by the locking and unlocking signals.
2.2.2 Dynamic CVT Modeling Based on Efficiency 
2.2.3 Modeling of Torque Acting on the Drive Wheel
2.3 Driving Resistances Modeling 
2.3.1 Rolling Resistance Modeling
2.3.2 Air Resistance Modeling
2.3.3 Ramp Resistance Modeling
2.3.4 Acceleration Resistance Modeling
2.4 Torque Losses of Other Components
All parameters above are based on a test car, which was also used to run drum tests.
3.1 Loss Measurements of Bench Test Input Equipment
The losses of input equipment, such as the loss of bearings in the bearing seat and the loss of input coupling, are defined as the torque losses between the input torque sensor and the input terminal of CVT without mounting the CVT box. The data of the input terminal were measured by using the input torque and speed sensor when the input motor worked at different speeds (1000, 1500, 2000, 2500,…max). Each test condition was repeated thrice, and the average of the results measured from these three tests was recorded as the torque loss of input equipment.
3.2 Loss Measurements of Bench Test Output Equipment
3.3 Bench Test Performance and CVT Efficiency Calculation
Bench test was performed with an existing CVT. Oil temperature was controlled within the range of 80±2 °C and monitored with a temperature sensor in real time while running the test. Cooling fans controlled by the temperature sensor were used to dissipate heat from the CVT and run the test at appropriate temperatures. Each test condition must be measured thrice to reduce test errors. Each measurement lasted for 10 s with a sampling period of 0.05 s. Five ratio points, namely, 2.631, 1.5, 1.05, 0.7, and 0.378, were evenly selected to run the bench test. The input torque was set at 20 N∙m to 200 N∙m, with an interval of 20 N∙m. The input speed was set at 1000 r/min to 3000 r/min, with an interval of 500 r/min.
The output torque of the CVT output terminal was converted to the input terminal of CVT and then compared with the input torque. The difference between them represented the torque loss of CVT, which was converted to the input terminal of CVT.
3.4 CVT Efficiency Modeling
The average efficiency of the test CVT will be 87.71% if CVT efficiency is evaluated based on the arithmetic average of different ratios. However, such an evaluation cannot determine the changes in CVT efficiency and will reduce the accuracy of the simulation model. Therefore, we used CVT efficiency maps as the efficiency model of the driveline simulation model in this study. Real-time CVT efficiency was obtained based on the piecewise programming lookup method.
CVT efficiency insignificantly changes with an increase input speed when input torque is over 60 N∙m, as shown in Figures 5 and 6. Therefore, CVT efficiency can be approximately expressed as a single-variable function of input torque. However, CVT efficiency significantly changed with a decrease in ratio. In the practical CVT control process, we can maintain a constant engine speed and adjust vehicle speed by adjusting the ratio. Ratio change is the main factor that causes a change in CVT efficiency in the control process. An independent variable that affects CVT efficiency should be selected in the real-time efficiency simulation model. Therefore, selecting ratio and input torque as the independent variables of CVT efficiency can improve accuracy. The speed intervals of the piecewise programming lookup were divided based on input speed. Real-time CVT efficiency can be obtained from the efficiency map at a certain speed interval based on current ratio, input speed, and input torque.
4.1 Driveline Simulation System Structure
4.2 CVT Variogram Generation
The region of the CVT variogram shown in Figure 10 is surrounded by the maximum ratio curve, the variation curve of the CVT ratio that changes with vehicle speed, the maximum vehicle speed curve and throttle opening at 0% and 100%, and the minimum ratio curve.
The CVT variogram such as the one shown in Figure 10 is the optimal economic variogram that can be generated by using optimization software tools and an optimization algorithm. The quantity of calibration test works via bench tests and road tests can be decreased because of this feature .
Numerous researchers have studied transmission efficiency and fuel economy by conducting powertrain bench tests using a real gasoline engine as the power source and dynamometers as the load sources in addition to a gearbox and drive axles [7, 13]. Achieving driving cycles in bench tests are relatively more complicated than that in simulation models. Bench tests that use gasoline engines and dynamometers can be displaced by the forecast simulation model. In addition, matching system parameters with existing test data of powertrain components using the simulation model is more convenient than bench tests.
The simulation test aims to verify the forecast accuracy of the car driveline simulation model based on CVT efficiencies measured from a bench test. The fuel consumption tests include driving cycles and constant velocity condition.
5.1 Driving Cycle
5.2 Constant Velocity Condition
Constant velocity tests should include 90 km/h and 120 km/h constant velocity tests according to the China National Standard GB/T12545.1-2008 . In this study, we ran constant velocity simulation tests at speeds of 60, 90 and 120 km/h in the car driveline simulation model.
Likewise, driving cycles and constant velocity conditions were run in real car drum tests in Section 6.
The car driveline simulation model was set with a load of 100 kg. Corners, ramps, and tire sliding were not considered while running the simulations. The velocity–time curves under different driving conditions were inputted into the driver model. The driveline simulation model tracked target vehicle speed according to the process shown in Figure 7. Transient and cumulative fuel consumptions in the current period were calculated according to the actual demands of engine speed and output torque. calculated in the simulation.
6.1 Real Car Drum Test
6.1.1 Parameters of Chassis Dynamometer and Test Car
Parameters of four-drum chassis dynamometer
Specification and model
Burke E. Porter Machinery Company U.S.A, Model 3260
Drum diameter (mm)
1219.2 ± 0.3
Range of axle base (mm)
Absorbing power (kW)
≥ 140 × 2
Measure range of torque (N·m)
Maximum speed (km/h)
Maximum load (kg)
Inertia mass simulation range (kg)
Inertia mass simulation accuracy
≤ ± 1%
Data to be collected in drum tests
Engine output torque
Real-time fuel consumption calculated by ECU
Throttle opening degree
Vehicle emission test system
Vehicle emission test system
Real-time fuel consumption
Vehicle emission test system
Parameters of test car
Maximum power of engine (kW)
Axle base (mm)
Front wheel base (mm)
Rear wheel base (mm)
Curb weight (kg)
Maximum speed (km/h)
6.1.2 Driving Resistance Setting and Drum Test
The rear wheels of the test car were fixed and electronic stability control (ESC) was turned off. Hence, the test car could be driven in a front-wheel drive mode. An experienced driver and a front passenger, whose weights were approximately 100 kg, were asked to ride the test car.
Drum tests were conducted based on the velocity–time curves of the driving cycles. The driver controlled the acceleration and brake pedals to maintain actual vehicle speed between the upper and lower limits of the velocity–time curve. Each driving cycle was run twice. The data recorded by the emission test system of the dynamometer were used as the test result. The cumulative fuel consumption at the end of each driving cycle was recorded, and the average fuel consumption of each cycle was considered as the effective test value.
Constant velocity tests were conducted at 60, 90, and 120 km/h according to the China National Standard GB/T12545.1-2008. The constant velocity test at each vehicle speed, which included a driver control mode and an automatic cruise mode, was run twice. The average fuel consumption in the two test modes was recorded.
6.1.3 Results of Real Car Drum Test
Results of real car drum test
Number of measurement
Result Qs (L/100 km)
All the test results of each condition in the two tests are sufficiently effective. All the results are listed as the average value of each condition to simplify the comparison and analysis processes.
6.2 Contrastive Analysis and Discussion
One set of the effective drum test results is compared with the simulation results (Figures 16, 17, 18, 19, 20, 21, 22, 23). The figures also show the changes in real input torque, input speed, and real ratio in the bench test. The simulation results agree well with the real values, which indicates that the simulation forecast model is sufficiently specific to forecast fuel economy. However, errors between the simulation results and drum test results still exist, which can lead to errors in fuel economy forecast.
Fuel consumption in different driving cycle
Drum test result (L/100 km)
Simulation result (L/100 km)
Compared with the results under constant velocity condition, a larger error in fuel economy forecast occurs under high-speed condition with a low ratio, such as 120 km/h, thereby resulting in a larger error of over 5% in fuel economy forecast in driver control mode. Less periods of high-speed condition close to 120 km/h occur in driving cycles, and thus, high-speed errors that can cause larger forecast errors do not significantly influence the simulation. The car mostly runs at a vehicle speed below 100 km/h, and the simulation model can maintain good accuracy with errors smaller than 4.2%. Therefore, the simulation results of the driving cycles exhibit better accuracy.
Assumptions and ideal simplifications of simulation model can cause the simulation errors. In addition, the errors between the simulation results and the real car drum test are simultaneously attributed to the CVT variogram and CVT efficiency model. The variogram used for the simulation of fuel economy should be an optimal economic variogram. In this study, such variogram is generated based on the original CVT variogram at incomplete throttle openings. However, the variograms used in the Transmission Control Unit (TCU) of the real car calibrated by the transmission factory are more complicated and consider more driving conditions, temperature conditions, and altitude conditions. The variogram used in the simulation is optimized based on the boundary conditions of vehicle performance, road conditions and the driving behavior of the driver, but numerous elements such as temperatures and altitudes are not fully considered. Consequently, the errors between the variogram generated in this study and the variogram used in the TCU are unavoidable. The contrastive results shown in Figures 16, 17, 18, 19, 20, 21, 22, 23 indicate that the simulation ratio does not completely follow the real ratio, thereby resulting in errors in input torques and input speeds between the simulations and the real car drum tests, which can lead to fuel economy calculation errors. Similarly, the contrastive results of the ratios, input torques, and input speeds under constant velocity condition shown in Figures 25, 26 indicate that the errors are attributed to the same reason. Moreover, the ratio errors between the simulation results and drum test results affect fuel economy forecast more evidently, particularly at 90 km/h and 120 km/h in driver control mode. In addition, the CVT efficiency model is based on the efficiency bench test within an ideal oil temperature range of 80±2 °C and the effects of temperatures on transmission efficiency are also not considered in the preceding assumption. The oil temperatures collected from CAN signals in the real car drum test are between 69 °C and 109 °C, which are influenced by working time and conditions. The longer the time and the higher the speed of CVT runs, the higher the oil temperature is. Therefore, the errors between the temperatures of the simulations and drum tests lead to the errors in real-time CVT efficiencies, which affect fuel economy forecast. To decrease errors, a better optimization algorithm that considers more conditions should be used to generate a better CVT variogram and a better CVT efficiency model that considers more temperature conditions should be built in future works.
A method for combining computer simulation forecasts with bench tests is proposed. A car driveline simulation model based on CVT efficiencies measured from a bench test and the driveline data of a real CVT car is constructed to forecast the fuel economy of a car equipped with a CVT box. The proposed method and model can simplify the process of studying the effects of CVT efficiency on car fuel economy compared with that through bench and road tests.
A CVT efficiency model based on the function of the piecewise programming lookup derived out from bench test data is developed. The car driveline simulation model can calculate the changing value of CVT efficiency in real time based on real-time ratio, input speed, and input torque.
An optimal economic variogram based on the original CVT variogram, the boundary conditions of vehicle performance, road conditions, and the driving behavior of the driver is generated and inputted into the simulation model as the CVT control strategy. The variogram exhibits good performance in the simulations.
Driving cycles and constant velocity condition are compared between the simulation results and the drum test results. The car driveline simulation model demonstrates better accuracy in terms of driving cycles and constant velocity condition at speeds below 120 km/h. The largest error of the results between the car driveline simulations and the real car drum tests for driving cycles is 4.099%, which is 5.449% under constant velocity condition in driver control mode and 4.2% under constant velocity condition in automatic cruise mode. Good agreements among the ratios, input speeds, and input torques are found between simulation results and the real car drum test results. The contrastive results confirm that the car driveline simulation model based on CVT efficiencies, which are measured from a bench test, exhibits good performance and can accurately forecast fuel consumption under different driving conditions.
Y-LL, KL and Y-ZJ were in charge of the whole trial; Y-ZJ wrote the manuscript; Y-ZJ, YF, KL, YZ and Z-JL assisted with sampling and laboratory analyses. All authors read and approved the final manuscript.
Yu-Long Lei, born in 1970, is currently a full professor at State Key Laboratory of Automotive Simulation and Control, College of Automotive Engineering, Jilin University, China. He received his PhD degree from Jilin University, China, in 1999. His research interests include vehicle automatic transmission system and automotive hydrodynamic drive.
Yu-Zhe Jia, born in 1991, is currently an engineer at Faw Jiefang Automotive Co., Ltd, Changchun, China. He received his master degree from Jilin University, China, in 2017. His research interests include automotive powertrain system theory and control.
Yao Fu, born in 1986, is currently a teacher at State Key Laboratory of Automotive Simulation and Control, College of Automotive Engineering, Jilin University, China. He received his PhD degree from Jilin University, China, in 2015. His research interests include vehicle automatic transmission theory and control technology.
Ke Liu, born in 1990, is currently a PhD candidate at State Key Laboratory of Automotive Simulation and Control, College of Automotive Engineering, Jilin University, China. He received his bachelor degree from Changan University, China, in 2012. His research interests include automotive powertrain system theory and control.
Ying Zhang, born in1990, is currently an engineer at United Automotive Electronic Systems Co., Ltd, Shanghai, China. He received his master degree from Jilin University, China, in 2018. His research interests include automotive powertrain system theory and control.
Zhen-Jie Liu, born in 1986, is currently a researcher at China North Vehicle Research Institute, Beijing, China. He received his PhD degree from Jilin University, China, in 2013. His research interests include vehicle automatic transmission theory and control technology.
The authors declare that they have no competing interests.
Supported by National Natural Science Foundation of China (Grant No. 51575220), and International S&T Cooperation Program of China (Grant No. 2014DFA71790).
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- D Z Zhang, Z Q Tang, Y D Zhang, et al. The simulated program of vehicle dynamics and fuel economy. Automotive Engineering, 1985(3): 2–12. (in Chinese)Google Scholar
- Y Lin. Automotive performance optimization method and program design. Bus Technology and Research, 1996, 18(3): 131–137. (in Chinese)Google Scholar
- S Akehurst, N D Vaughan, D A Parker, et al. Modeling of loss mechanisms in a pushing metal V-belt continuously variable transmission, part 1 torque losses due to band friction. Proc. Inst. Mech. Eng., Part D: Journal of Automobile Engineering, 2004, 218(11): 1269–1281.Google Scholar
- S Akehurst, N D Vaughan, D A Parker, et al. Modeling of loss mechanisms in a pushing metal V-belt continuously variable transmission, part 2 pulley deflection losses and total torque loss validation. Proc. Inst. Mech. Eng., Part D: Journal of Automobile Engineering, 2004, 218(11): 1283–1293.Google Scholar
- S Akehurst, N D Vaughan, D A Parker, et al. Modeling of loss mechanisms in a pushing metal v-belt continuously variable transmission, part 3 belt slip losses. Proc. Inst. Mech. Eng, Part D: Journal of Automobile Engineering, 2004, 218(11): 1295–1306.Google Scholar
- N S Cheng, W Liu, D Z Guo, et al. Experimental study of transmission efficiency for metal pushing V-belt type CVT. Journal of Northeastern University (Natural Science), 2000, 21(4): 394–396. (in Chinese)Google Scholar
- H C Huang, J C Xu, J B Wen. Test and analysis CVT efficiency. Drive System Technique. 2010, 24(2): 37–41. (in Chinese)Google Scholar
- T Wang, H L Li. Research on the influence of gear precision on the transmission efficiency. Bus & Coach Technology and Research, 2015(5): 56–59. (in Chinese)Google Scholar
- Y Gao. The influence factor analysis and experimental study of the mini-car gearbox transmission efficiency. Wuhan: Wuhan University of Technology, 2013. (in Chinese)Google Scholar
- P P Li, H Y Dou, L F Zhang, et al. Analysis and experimental research of oil influence on gear transmission efficiency. Agricultural Equipment & Vehicle Engineering, 2014, 52(5): 56–60. (in Chinese)Google Scholar
- P D Patel, J M Patel. An experimental investigation of power losses in manual transmission gear box. International Journal of Applied Research in Mechanical Engineering, 2012, 2(1): 1–5.Google Scholar
- C Zhu, H Liu, J Tian, et al. Experimental investigation on the efficiency of the pulley-drive CVT. International Journal of Automotive Technology, 2010, 11(2): 257–261.View ArticleGoogle Scholar
- Y Luo, D Y Sun, D T Qin, et al. Fuel optimal control of CVT equipped vehicles with consideration of CVT efficiency. Journal of Mechanical Engineering, 2010, 46(4): 80–86. (in Chinese)View ArticleGoogle Scholar
- Y C Cai. Research on the fuel economy and system reliability of metal V-belt CVT. Changsha: Hunan University, 2011. (in Chinese)Google Scholar
- Xi Wang. Research of automobile fuel economy based on transmission efficiency. Chongqing: Chongqing University, 2010. (in Chinese)Google Scholar
- F W Yan, F Hu, S P Tian, et al. Simulation and parameter sensitivity analysis of automobile fuel economy. Journal of Wuhan University of Technology (Information & Management Engineering), 2010, 32(2): 261–264. (in Chinese)Google Scholar
- D Y Huang. A feasible method for forecasting fuel economy. Journal of Jiangsu University of Science and Technology, 1996, 17(4): 16–19. (in Chinese)Google Scholar
- X Y Yang, G Wang, R K Zhou, et al. Research and simulation analysis method of automobile fuel economy. Journal of Chongqing University of Technology (Natural Science), 2014, 28(8): 6–12. (in Chinese)Google Scholar
- J Gong, D X Hao, Y Chen, et al. Study on shift schedule saving energy of automatic transmission of ground vehicles. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2004, 5(7): 878–883.View ArticleGoogle Scholar
- J G Cao, D T Qin, J J Hu, et al. Application of hydraulic torque converter incorporated in mechanical continuously variable transmission. Journal of Chongqing University (Natural Science Edition), 2002, 25(8): 5–9. (in Chinese)Google Scholar
- L G CHEN, S M LIU, Y F ZHENG, et al. Simulation and test of the lock process for hydrodynamic torque converter. Chinese Hydraulic and Pneumatics, 2012, (4): 27–29. (in Chinese)Google Scholar
- H S Zhang, Y Shi, L L Qin, et al. Slip control strategy of torque converter lock-up clutch. Mechanical Engineering and Automation, 2015, 6: 21–23. (in Chinese)Google Scholar
- N S Cheng. Principle and design of automotive metal continuously variable transmission–CVT. Beijing: China Machine Press, 2008: 166–167. (in Chinese)Google Scholar
- Z S Yu. Automotive theories. 5th Edition. Beijing: China Machine Press, 2009: 7-19. (in Chinese)Google Scholar
- T J Fu. Study on intelligent control of metal pushing V-belt type CVT. Changchun: Jilin University, 2004. (in Chinese)Google Scholar
- Z P Yang. Cruise software using vehicle power and fuel economy simulation analysis. Automobile Applied Technology, 2015(1): 107–109. (in Chinese)Google Scholar
- P Schoeggl, W Kriegler, E Bogner. Virtual optimization of vehicle and powertrain parameters with consideration of human factors. 2005 SAE World Congress, Detroit, Michigan, April 11–14, 2005.Google Scholar
- M Andre. Driving cycles development: characterization of the methods. SAE International Spring Fuels and Lubricants Meeting, Dearborn, Michigan, May 6-8, 1996.Google Scholar
- J W Zhang, M L Li, G H Ai, et al. A study on the features of existing typical vehicle driving cycles. Automotive Engineering, 2005, 27(2): 220–224. (in Chinese)Google Scholar
- F X Zhang. Study on driving cycles of city vehicle. Wuhan: Wuhan University of Technology, 2005. (in Chinese)Google Scholar
- T Ma. Research on test method of hybrid electric vehicle motor based on driving cycle. Dalian: Dalian University of Technology, 2011. (in Chinese)Google Scholar
- General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China; Standardization Administration of the People’s Republic of China. GB/T 12545.1—2008. Measurement methods of fuel consumption for automobiles-Part 1: Measurement methods of fuel consumption for passenger cars. Beijing: China Standard Publishing House, 2009. (in Chinese)Google Scholar