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

2025/06/09 by Amirreza Yasami, Yasami, Amirreza, Mahdi Shahbakhti +4
Chemical Engineering · Engineering · #Advanced Combustion Engine Technologies #Electric and Hybrid Vehicle Technologies #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Systems and Control (eess.SY) #Vehicle emissions and performance #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.07929

openalex publication_date 2025/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Managing engineering systems with large state and action spaces through deep reinforcement learning
• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement\n Learning
• What Is a Savitzky-Golay Filter? [Lecture Notes]
• Reinforcement Learning: An Introduction

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