The construction of representative driving cycles is crucial for vehicle energy consumption analysis, emission evaluation, and control strategy optimization. However, classical approaches face trade-offs between exploration scope, computational efficiency, and perturbation controllability. This study proposes a novel Markov Chain Divergence-Convergence Domain (MC-DCD) method to address these challenges. The proposed approach first implements reachable domain screening for candidate cycle sub-segments through divergence-convergence domain analysis, ensuring thorough exploration of potential optimal solutions. Subsequently, it achieves minimal perturbation to existing high-quality cycles through rapid reconstruction of local segments in suboptimal cycles. Validated using 1 million seconds of real-world driving data from Qingdao, the MC-DCD method generated 100,000 candidate segments in just 0.66 s. The final driving cycle showed an average statistical deviation of only 0.11%. Compared with classical Markov Chain (MC) and Markov Chain-Genetic Algorithm (MC-GA) methods, the proposed approach improved accuracy by 97-fold and 12.5-fold, respectively, while achieving over 100-fold and 10-fold acceleration in candidate cycle generation speed. Beyond enhancing traditional vehicle evaluation, the massive high-fidelity datasets generated by this method serve as a critical training foundation for neural network-based models, effectively mitigating the challenge of large-scale data scarcity.
Meng et al. (Thu,) studied this question.