Designing Heavy-Duty Vehicles’ Four-Parameter Driving Cycles to Best Represent Engine Distribution Consistency

Abstract

The driving gear affects engine transient cycles. Current methods of designing engine transient cycles need to establish the shift model of the transmission system, and the differences from the actual driving shift law result in the quantitatively low consistency of engine transient test cycles. Then the representativeness and accuracy of the designed engine transient and steady-state test cycles will be worse. By expanding the Markov chain evolution (MCE) framework, in this study, four-parameter driving cycles with gear for heavy-duty vehicles are designed, which can represent the consistency of the engine cycle distribution. The Markov chain model-based multi-parameter state transition with gear information is constructed to be used as constraints to design the genetic operators; engine characteristic model-based parameters are calculated as the constraints for designing the objective function. Multi-parameter vehicle driving cycles are thus generated by the expanded MCE framework and then transformed into engine transient test cycles. The designed driving cycles were verified and analyzed using the data collected from a heavy-duty vehicle. The results showed that the driving parameters and fuel consumption per 100 km between the designed driving cycles and the collected database met the threshold deviation; the correlation coefficients of the distributions related to gear utilization, vehicle specified power (VSP), and engine cycles reached as high as 90%; and multiple results had the same effect as mentioned above. Compared with a conversion method based on the economical shift rule, the fuel consumption rate distribution in this study can be closer to the actual engine running conditions. INDEX TERMS Driving gear, engine distribution, heavy-duty vehicle, vehicle driving cycles, fuel consumption NOMENCLATURE MCE Markov chain evolution VSP Vehicle specified power/ (Kw/ton) WHSC World harmonized steady-state cycle TPM Transition probability matrix PKE Positive kinetic energy per distance/ (m/s 2) OD-C Collected database of congested condition DC-C Designed cycle of congested condition CC-C Contrast cycle of congested condition OD-F Collected database of free-flow condition DC-F Designed cycle of free-flow condition CC-F Contrast cycle of free-flow condition OD-H Collected database of highspeed condition DC-H Designed cycle of highspeed condition CC-H Contrast cycle of highspeed condition 2D-NT Two-dimensional engine speed and torque distribution 3D-NT Three-dimensional engine speed and torque distribution

Using the Equal-Power-Based MCE to Transform Driving Cycles With Road Grade for Testing Heavy-Duty Vehicle Fuel Consumption on Dynamometer

“…The correlation coefficient of any two distributions is used as the index in evaluating the consistency of the engine distribution between the original database and the expected driving cycles. The engine speed and torque can be calculated from the driving cycles of the three methods and basic parameters of the vehicle [22]. The interval of the engine speed and engine torque is then set at 100.…”