Look-ahead gear-shifting strategy on ramps for heavy trucks with automated mechanical transmission

Abstract

Look-ahead information has been applied to vehicle shift systems and has led to the development of innovative changes in shifting strategies. A shifting strategy significantly influences the dynamic and economic performance of a truck. If a shift control system can predict the information of the road ahead, then the dynamic programming method can be used to obtain an optimal shift schedule, thereby achieving the best balance between the dynamic and the economic performance of a truck. Determining the weights of the economic and dynamic performance of a truck is critical in dynamic programming. Weight is related to road slope value; hence, this study uses different weights on 3%, 5%, and 10% ramp simulation experiments to investigate the relationship between fuel consumption and running time and to determine the optimum weight for different slopes. On the basis of the optimal shift schedule obtained via dynamic programming, the ramp information of the road ahead can be utilized to adjust the shift point to avoid unnecessary shifting before entering the slope and reduce shift times on a ramp. An actual truck test shows that the anthropomorphic shifting strategy proposed in this study meets driver expectations and is conducive to the ramp driving of trucks with automated manual transmission.

Research on gear decision method of commercial vehicle based on predictive road information

“…Neural network 4,15 Heavy-duty vehicle -Not considering changes in real-time information Rain flow counting 5 Heavy-duty vehicle -Not considering road conditions Genetic algorithm 6,7,10 Electric vehicle Driving style Different fuels Not considering road conditions MPC 8,9,18 Electric vehicle Vehicle speed Not considering road conditions Adaptive control 11 Electric vehicle Driving style Not considering changes in real-time information Greedy algorithm 12 Passenger car --K-means clustering algorithm 13 Electric bus -Not considering changes in real-time information Shifting map 14 Passenger car Driving intention Cannot be optimized online DRNN 15 Passenger car -Not considering changes in real-time information Dynamic programming 16 Heavy-duty vehicle -Slope shift strategy only considered Equivalent motor efficiency 17 Electric bus -For special drivetrain…”

“…The results show that DRNN has higher accuracy and adaptability than the neural network. 15 Based on the dynamic programming algorithm, Cong et al 16 designed the shifting strategy of different road slopes, adjusted the weight of economy and power according to the road slope value, and avoided unnecessary shifting before entering the slope and frequent shifting on the slope. Nguyen et al 17 proposed a new shift strategy for a dual-motor power system, which selected gears according to the equivalent motor efficiency to ensure maximum efficiency.…”

Research on the Prediction Model of Engine Output Torque and Real-Time Estimation of the Road Rolling Resistance Coefficient in Tracked Vehicles

“…Because the established engine output torque prediction model was too complex, this method was difficult to use for real vehicle control. Cong et al [14] established a look-up table model of the engine output torque by fitting the corresponding relationship between the engine output torque, engine speed, and accelerator pedal position and estimated the road slope based on the Kalman filter algorithm. This method required a lot of manual calibration work in the bench test stage of the vehicle, and it was difficult to update after the calibration was complete.…”