A Generative Physics-Informed Reinforcement Learning-Based Approach for Construction of Representative Drive Cycle
Abstract
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.
Cite
@article{arxiv.2506.07929,
title = {A Generative Physics-Informed Reinforcement Learning-Based Approach for Construction of Representative Drive Cycle},
author = {Amirreza Yasami and Mohammadali Tofigh and Mahdi Shahbakhti and Charles Robert Koch},
journal= {arXiv preprint arXiv:2506.07929},
year = {2025}
}