A Generative Physics-Informed Reinforcement Learning-Based Approach for Construction of Representative Drive Cycle

• URL: http://arxiv.org/abs/2506.07929v1
• Date: Mon, 09 Jun 2025 16:44:42 GMT
• Title: A Generative Physics-Informed Reinforcement Learning-Based Approach for Construction of Representative Drive Cycle
• Authors: Amirreza Yasami, Mohammadali Tofigh, Mahdi Shahbakhti, Charles Robert Koch,
• Abstract summary: PIESMC constructs driving cycles by capturing transient dynamics, acceleration, deceleration, idling, and road grade transitions.<n>It delivers efficient cycle construction with reduced computational cost.<n>It is nearly an order of magnitude faster than conventional techniques.
• Score: 0.5999777817331317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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