The field of sustainable energy and transportation systems is rapidly advancing, with a focus on optimizing the performance and efficiency of electric vehicles, battery management, and smart charging infrastructure. Researchers are exploring innovative approaches, such as physics-informed reinforcement learning and multi-agent deep reinforcement learning, to improve the reliability and scalability of these systems. Notably, the integration of physical grid constraints and voltage-based reward design into reinforcement learning algorithms is enabling more effective and realistic modeling of complex energy systems. Furthermore, the development of novel frameworks for dynamic routing and scheduling of latency-critical services is enhancing the delivery of time-sensitive information over dynamic networks.

Some noteworthy papers in this area include: The paper on SDG-L, which proposes a semiparametric deep Gaussian process regression framework for battery capacity prediction, achieving an average test MSE error of 1.2%. The paper on Optimal Multi-Modal Transportation and Electric Power Flow, which develops a novel framework for coordinated dynamic operation of transportation-electricity nexus, demonstrating substantial value over siloed infrastructure management. The paper on Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging, which introduces a physics-informed RL algorithm that integrates a differentiable power flow model and voltage-based reward design, outperforming model-free RL and optimization-based baselines in grid constraint management and user satisfaction.