Using mobile charging stations as probes to discover latent EV charging demand in stochastic environments: A deep reinforcement learning approach

The increasing adoption of electric vehicles underscores the urgent need for efficient and reliable charging infrastructure. Fixed charging stations are crucial for meeting surging demand and ensuring convenient access, yet accurately predicting demand remains highly challenging, because charging patterns change in response to where stations are placed, which creates a cyclical dilemma for planning. However, mobile charging stations (MCSs) offer a novel solution by flexibly relocating across urban areas, they can both deliver energy and act as dynamic probes to collect real-time data on charging demand. Existing studies, however, typically assume prior knowledge of demand distributions, which is rarely available in emerging EV markets or where privacy concerns limit data access. This paper proposes a Deep Reinforcement Learning (DRL) approach, formulated as a Partially Observable Markov Decision Process (POMDP), to optimize the relocation of MCSs in conjunction with fixed charging stations, while simultaneously uncovering latent demand patterns. We employ an Advantage Actor–Critic (A2C) algorithm with Long Short-Term Memory (LSTM) networks to capture temporal dependencies and adapt to stochastic demand. A dynamic Mixed-Integer Programming (MIP) model is developed as a benchmark that represents an idealized case with perfect foresight of demand. We compare the DRL agent against this optimization model in two settings: (i) a synthetic toy environment for controlled testing, and (ii) a realistic simulation of the Frederiksberg municipality in Denmark, calibrated with real charging data. The results show that the DRL framework effectively adapts to stochastic demand, outperforms the optimization baseline under uncertainty, and scales efficiently to larger problem instances. Beyond methodological contributions, the findings highlight how MCSs can serve a dual role as infrastructure supplements and as demand-discovery tools, offering valuable insights for data-driven and adaptive EV charging planning.