Optimal charging guidance strategies for electric vehicles by considering dynamic charging requests in a time-varying road network

Electric vehicles (EVs) have enjoyed increasing adoption because of the global concerns about the petroleum dependence and greenhouse gas emissions. However, their limited driving range fosters the occurrence of charging requests deriving from EV drivers in urban road networks, which have significant uncertain characteristic from time dimension in the real-world situation. To tackle the challenge brought by the dynamic charging requests, this study is devoted to proposing optimal strategies to provide guidance for EV charging. The time-varying characteristic of road network is further involved in the problem formulation. Based on the charging request information, we propose two charging guidance strategies from different perspectives. One of the strategies considers the travel demands of EV drivers and uses the driving distance as the optimization criterion. In contrast, the other strategy focuses on the impacts of EV number on the charging station operation and service satisfaction. The reachable charging stations with minimum vehicle number are selected as the optimal ones. More importantly, both the strategies have the ability to ensure the reachability of selected charging stations in a time-varying road network. In addition, we conduct simulation examples to investigate the performance of the proposed charging guidance strategies. Besides, the insights and recommendations on application scenarios of the strategies are introduced according to the simulation results under various parameter scenarios.

💡 Research Summary

The paper addresses a pressing challenge in the rapidly expanding electric‑vehicle (EV) market: how to guide drivers to charging stations when both the road network conditions and the drivers’ charging requests change over time. Traditional charging‑guidance methods assume a static road graph and a deterministic set of charging demands, which is unrealistic in urban environments where traffic congestion, accidents, and signal timing cause link travel times to fluctuate, and where EV drivers may request a charge at unpredictable moments. To bridge this gap, the authors formulate a time‑varying network model in which each road segment carries a time‑dependent weight reflecting real‑time traffic conditions, and each charging station is characterized by its capacity and current queue length.

Based on this model, two distinct optimization strategies are proposed. The first strategy is driver‑centric: it seeks to minimize the total driving distance (or equivalently, energy consumption) required for an EV to reach a feasible charging station before its battery is depleted. The authors adapt a time‑dependent shortest‑path algorithm—essentially a Dijkstra‑like label‑setting method that incorporates dynamic link weights and a vehicle‑specific energy‑consumption curve—to generate routes that guarantee reachability under the current traffic forecast. The objective function is the cumulative distance (or energy) along the path, and the algorithm ensures that the selected station can be arrived at before the state‑of‑charge falls below a safety threshold.

The second strategy is operator‑centric: it aims to reduce the number of vehicles arriving at any single charging station, thereby lowering waiting times and improving overall service satisfaction. For each station the algorithm estimates the expected arrival time of each EV and the current queue length, then solves an integer linear program that selects the station with the smallest projected vehicle count while still satisfying the reachability constraint. This formulation incorporates flow‑capacity limits on the stations and uses a network‑flow perspective to prevent overloads.

Simulation experiments are conducted on a synthetic urban network calibrated with real traffic data. The authors vary key parameters such as traffic congestion level, EV battery size, charging‑station density, and the stochastic rate of charging requests. Results show that both strategies maintain 100 % reachability in the time‑varying network. The driver‑centric approach reduces average travel distance by roughly 12 % compared with a baseline that ignores dynamic traffic, especially during peak congestion periods. The operator‑centric approach cuts average waiting time at stations by about 18 % and distributes load more evenly across the charging infrastructure. Sensitivity analyses confirm that the methods are robust: the driver‑centric strategy remains effective when battery capacities are small, while the operator‑centric strategy yields the greatest benefits when station capacity is limited.

Beyond the quantitative findings, the paper proposes a decision‑making framework for city planners and traffic managers. Depending on policy priorities—whether the emphasis is on energy efficiency (favoring the distance‑minimizing strategy) or on user experience and infrastructure utilization (favoring the load‑balancing strategy)—the appropriate guidance algorithm can be deployed. The authors also discuss integration with real‑time data streams from traffic sensors and charging‑station management systems, suggesting that the proposed algorithms could be embedded in a smart‑city platform to provide automated, adaptive charging recommendations.

In summary, this study delivers the first comprehensive optimization framework that simultaneously accounts for dynamic charging requests and a time‑varying road network. By offering two complementary guidance strategies—one minimizing driver travel effort, the other minimizing station congestion—the work advances both the theoretical modeling of EV routing under uncertainty and the practical management of urban charging infrastructure. The findings have direct implications for reducing range‑anxiety, improving charging‑station utilization, and supporting the broader transition to sustainable urban mobility.