Shared learning of powertrain control policies for vehicle fleets

arXiv:2404.17892v1 Announce Type: cross
Abstract: Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-the-field learning of powertrain control policies that optimize fuel economy and other performance metrics. Indeed, they have shown great potential in this regard for individual vehicles on specific routes or drive cycles. However, for fleets of vehicles that must service a distribution of routes, DRL approaches struggle with learning stability issues that result in high variances and challenge their practical deployment. In this paper, …

abstract aim arxiv control cs.ai cs.lg cs.sy data data-driven drive economy eess.sy however indeed metrics performance policies regard reinforcement reinforcement learning routes type vehicles