CyGen-SAC: A data-driven framework for representativeness metric and policy optimization in generation of multidimensional driving cycles

Accurate development, optimization, and validation of vehicle control systems — including energy management systems (EMS) — rely on driving cycles that faithfully reproduce real-world operating conditions. Multidimensional cycles — incorporating variables such as yaw rate and road grade — capture critical dynamics beyond conventional speed profiles but lack both rigorous definitions of representativeness and reliable generation methods. This absence of structurally representative cycles can reduce the effectiveness of EMS optimization and result in significant gaps between simulation-based evaluations and actual in-vehicle performance. To address this issue, we propose CyGen-SAC , a data-driven framework for generating multidimensional driving cycles via deep reinforcement learning (DRL). At its core, a representativeness metric is constructed from a selected set of bivariate joint distributions, which preserve the key dependencies of the six-dimensional data and capture the joint relationships observed in real-world driving. Embedded as a structured reward, this metric provides dense feedback to a Soft Actor-Critic (SAC) agent, enabling the generation of driving cycles that are both distributionally aligned and highly representative. Evaluated on real-world driving data, CyGen-SAC demonstrates significant gains in distributional alignment, achieving a 6.6% improvement for the best single cycle and 23.4% across the generated set relative to a baseline method. For power-demand characteristics on a representative cycle, the average error relative to aggregate statistics from real-world measurements decreases from 43.2% to 5.8%. This improvement enhances the credibility of EMS evaluation and can be readily transferred to other control-oriented time-series tasks under structural constraints.