用物理约束强化学习生成更真实的驾驶循环，提升效率与精度。
A Generative Physics-Informed Reinforcement Learning-Based Approach for Construction of Representative Drive Cycle
- 结合物理规律与强化学习，通过蒙特卡洛采样构建驾驶循环。
- 相比现有方法，动态误差降低超57%，计算速度提升近10倍。
- 适合车辆设计、能效分析等需要高保真驾驶数据的场景。
精准的驾驶循环构建对车辆设计、燃油经济性分析和环境影响评估至关重要。本文提出一种生成式物理信息强化学习方法（PIESMC），通过捕捉瞬态动力学、加减速、怠速及道路坡度变化，确保模型保真度。该方法基于物理信息强化学习框架，结合蒙特卡洛采样，实现高效驾驶循环生成，显著降低计算成本。在两个真实数据集上的实验表明，PIESMC 在关键运动学与能量指标上表现优异，相较于基于微行程的MTB方法，累计运动学片段误差降低57.3%；相比马尔可夫链方法MCB，降低10.5%。同时，其运行速度接近传统方法的十分之一。车辆特定功率分布与小波变换频域分析进一步验证了其对实验中心趋势与变异性的真实还原能力。
原文摘要 · Abstract (English)
Accurate driving cycle construction is crucial for vehicle design, fuel economy analysis, and environmental impact assessments. A generative Physics-Informed Expected SARSA-Monte Carlo (PIESMC) approach that constructs representative driving cycles by capturing transient dynamics, acceleration, deceleration, idling, and road grade transitions while ensuring model fidelity is introduced. Leveraging a physics-informed reinforcement learning framework with Monte Carlo sampling, PIESMC delivers efficient cycle construction with reduced computational cost. Experimental evaluations on two real-world datasets demonstrate that PIESMC replicates key kinematic and energy metrics, achieving up to a 57.3% reduction in cumulative kinematic fragment errors compared to the Micro-trip-based (MTB) method and a 10.5% reduction relative to the Markov-chain-based (MCB) method. Moreover, it is nearly an order of magnitude faster than conventional techniques. Analyses of vehicle-specific power distributions and wavelet-transformed frequency content further confirm its ability to reproduce experimental central tendencies and variability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。