Aug 2026· Sustainability· 0 citations· 36 references
TL;DR
A hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem is proposed and demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charging infrastructure planning problems.
Abstract
The popularity of electric vehicles (EVs) is growing at a fast pace, creating a need for the strategic deployment of charging stations (CSs) to provide enough coverage, cost effectiveness, and compliance with grid and urban planning regulations. The deployment of large-scale infrastructure under multiple, often conflicting constraints remains a challenging engineering decision-making problem. In this paper, we propose a hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem. The proposed approach employs Q-learning and Deep Q-Network (DQN) agents to iteratively refine the initial deployment while simultaneously optimizing deployment cost, charging demand coverage, and operational utility under practical planning constraints. The constraints include grid capacity limitations, renewable energy utilization, and fairness considerations. The proposed framework is evaluated in realistic urban scenarios. The experimental results demonstrate that the reinforcement learning (RL) approach achieves superior trade-offs among competing objectives compared to baseline heuristic strategies, while maintaining computational scalability for large candidate location sets. The proposed framework demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charging infrastructure planning problems. The proposed methodology is scalable to other complex engineering planning and resource allocation problems characterized by multi-objective trade-offs and dynamic constraints. Beyond improving optimization performance, the proposed framework contributes to sustainable transportation planning by supporting the efficient deployment of electric vehicle charging infrastructure. Optimized charging station placement promotes greater accessibility to charging services, encourages electric vehicle adoption, reduces unnecessary travel associated with charging activities, and contributes to lower greenhouse gas emissions. Consequently, the proposed methodology provides decision-makers with a scalable and intelligent planning tool that supports the transition toward more sustainable and energy-efficient urban mobility systems.
With the large-scale integration of electric vehicles (EVs) into power grids, massive concentrated EV charging accelerates the loss of life (LOL) of distribution transformers. There is a need to reduce transformer LOL while optimizing user experience, thus formulating the EV charge optimization concerning transformer LOL (ECOTL) problem. However, due to its characteristics, such as multi-objective tradeoffs, nonlinearity, uncertainty, and the requirement for time-sensitive online control, the ECOTL problem poses severe challenges in obtaining a high-quality Pareto front, acquiring robust solutions, and improving solving speed. To address these, we propose a multi-objective multi-agent deep reinforcement learning (MOMADRL) method that deploys multiple Critics and Actors for each agent. During training, Critics learn value functions with distinct preferences and guide the corresponding Actors that specialize in learning optimal strategies to update their policies. After sufficient training, each Actor can determine a preference-specific charging strategy in the test phase. Case studies based on real electricity price and load data demonstrate that compared with traditional multi-agent deep reinforcement learning (MADRL) methods, the proposed method achieves better convergence performance on the training set. Moreover, it obtains better Pareto-front approximation than the learning-based baselines while providing fast online inference under the tested simulation conditions.
Electric Vehicles (EVs) are emerging as sustainable alternatives to internal combustion engine vehicles; however, efficient route planning remains a major challenge due to limited driving range, sparse charging infrastructure, and variable energy consumption patterns. Traditional shortest-path algorithms, such as Dijkstra’s and A*, often fail to account for EV-specific factors, including charging station availability, connector compatibility, and energy constraints. This study presents a comprehensive EV route optimization framework that integrates reinforcement learning (RL) with graph-based methods. A novel Dual Q–Adaptive Weighting model that balances reward and cost through a primal–dual learning mechanism is proposed. The framework learns energy-aware routing strategies from historical navigation experience. The model is compared against standard RL approaches—Q-Learning and Double Q-Learning—as well as enhanced variants of A* and Dijkstra’s algorithms that incorporate charging density and time-penalty considerations. Real-world EV charging infrastructure data from the Alternative Fuels Data Center (AFDC) and Placekey datasets are used to construct a clustered navigation graph via DBSCAN. Experimental results across multiple intercity routes show that the proposed Dual Q–Adaptive model achieves the highest route accuracy of 78.66%, outperforming Double Q-Learning (76.27%), Q-Learning (77.52%), and traditional A* (74.26%) and Dijkstra (60.92%) algorithms. A* and Dijkstra with modifications, use fewer charging stops than traditional algorithms. The Improvised algorithms provide substantial improvements over their baseline counterparts. The results demonstrate that reinforcement learning integrated with graph-theoretic optimization can enable scalable, infrastructure-aware, and efficient EV route planning.
Sarvesh Kumar, Rayappa David Amar Raj, Archana Pallakonda et al.· Scientific Reports· 0 citations
Results validate the effectiveness of bilevel RL for complex energy optimization problems, highlighting its potential as a scalable control paradigm for smart management systems.
D. Vamvakas, Christos D. Korkas, E. Kosmatopoulos· Energies· 0 citations
Electric Vehicles are thought to be among the best options for lowering gas emissions and oil consumption. EV users can from a charging station with a well-thought-out schedule and price plan. Advanced energy management techniques are required to guarantee the sustainable, dependable, and effective operation of charging infrastructure due to the quick rise in the usage regarding electric vehicle (EV). In light of recent research, current design restrictions as well as the erratic conduct of EV consumers make classic scheduling techniques, such as set costs for Time-of-Use (ToU), insufficient. Uncoordinated charging results Voltage instability caused by transformer overloading, also wasteful utilization of electricity from renewable as EV use rises. AI is becoming more widely acknowledged in a crucial facilitator of scalable, durable, effective EV charging facilities with intelligence. This paper proposes a hybrid AI-based architecture that integrates real-time Traffic pattern, distance of EV, arrival and departure time of EV state of charge as input. Through real-time monitoring and charge optimization, the EVCS enable intelligent EV charging. The AI framework employs a non-uniform Poisson process in order to dynamically assess user demand also enhances schedule of charging. While the charging demand of electric vehicles (EVs) is intrinsically heterogeneous, decentralized, and stochastic, the intermittent and weather-dependent nature of PV power results in considerable output uncertainty. The purpose of the EV Charging Grid Optimization is to facilitate research on AI-driven energy management for EV charging infrastructure. The two proposed optimization algorithm improves the operational effectiveness of EVCS. Using threshold value Reinforcement Learning make the real-time decision that dynamically schedule the EV. Threshold value is determined from customer preferences. The proposed technology demonstrates scalability, durability, and cost-effectiveness and provides a feasible substitute for upcoming metropolitan EV charging system.
Jose Devaraj, Daphni Paulphin J.· International journal of com...· 0 citations
We made a Physics-Informed Reinforcement Learning (PI-RL) framework to coordinate the charging stations for electric vehicles (EVs) in real time. These stations are powered by different renewable energy sources (RES), like wind and photovoltaic (PV). Our methodology explicitly integrates energy conservation laws, state-of-charge (SOC) dynamics, and inverter limitations into the training process, unlike previous reinforcement learning (RL)-based methodologies that are confined to single-source renewable energy systems (RES) and do not incorporate physical system constraints. We changed the Soft Actor-Critic (SAC) algorithm by adding domain-informed reward shaping and adaptive Lagrangian multipliers in order to make sure that constraints were met. We subsequently structured the EV-RES coordination issue as a physics-constrained Markov Decision Process (MDP). We evaluated the proposed PI-RL approach using actual datasets of solar irradiance, synthetic wind generation profiles, and electric vehicle arrival patterns. In terms of operational profit, safety (constraint violation rate), and use of renewable energy, our approach worked better than traditional SAC and model-based rolling optimization. Also, our model made it much less common for charge-discharge switching to happen, which led to control strategies that are easier to understand and that help the battery last longer. These results show that the PI-RL framework is a reliable and widely applicable way to manage energy in real time in EV charging infrastructures that are getting more and more complicated as they use renewable energy.
A collaborative optimization framework based on multi-agent reinforcement learning is proposed for orderly charging at electric vehicle charging stations and coordinated interaction with the power grid, providing a technical reference for intelligent charging coordination under grid interaction and electromagnetic compatibility constraints.
