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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.

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