A Generative Physics-Informed Reinforcement Learning-Based Approach for Construction of Representative Drive Cycle
This addresses the need for efficient and representative driving cycle construction for vehicle engineers and environmental analysts, though it is incremental as it builds on reinforcement learning and physics-informed methods.
The paper tackles the problem of constructing accurate driving cycles for vehicle design and analysis by introducing a generative Physics-Informed Expected SARSA-Monte Carlo (PIESMC) approach, which achieves up to a 57.3% reduction in cumulative kinematic fragment errors compared to existing methods and is nearly an order of magnitude faster.
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.