High-Accuracy Driving Cycle Construction with RL-MC-RRD: A Framework for Energy Consumption Benchmarking

The construction of typical driving cycles is essential for evaluating vehicle energy consumption, yet existing methods struggle to balance search coverage, computational efficiency, and disturbance control. This paper proposes a novel method based on the Markov Chain Resampling Reachable Domain with Reinforcement Learning (RL-MC-RRD). Reinforcement learning jointly determines the sub-segments to be refined and their corresponding bridging-path search budgets, followed by state lookup table-based resampling to further correct the residual statistical discrepancies. Validated using one million seconds of real driving data from Qingdao, the final driving cycle constructed by RL-MC-RRD achieved an average deviation of only 0.206% across 16 characteristic parameters. Compared to traditional MC and MC-GA methods, it improves construction accuracy by 50-fold and 8-fold. In addition, the RL-MC-RRD-generated driving cycle exhibited deviations of 1.309% in electric-energy consumption and 3.075% in fuel consumption relative to the real-world driving cycle and offers an effective approach to accurate driving cycle construction, vehicle energy management, and performance evaluation.