Can Information Design Resolve Electric Vehicle Charging Chaos?

Charging remains an obstacle to the mass adoption of electric vehicles (EVs), especially for long-distance travel. If many EV drivers take to the road around the same time, their “range anxiety” may create a self-fulfilling prophecy. As drivers anticipate uncertainty and congestion at charging stations, they tend to make decisions about when and where to charge that could collectively lead to chaos and inefficiency. Here, we show that information design can persuade drivers to adopt decisions that are better for the system while being consistent with their self-interest as defined by the Bayesian Nash equilibrium. Our stylized model incorporates a congestion effect into drivers’ payoffs and assumes that the information designer can leverage its private knowledge about the charging condition to influence heterogeneous drivers whose type is defined by their vehicle’s remaining range. We consider both public and private information designs. The former does not depend on the driver type, whereas the latter does. For the private design problem, we propose a novel cutoff structure that enables us to reformulate an infinite-dimensional problem as a finite one. Our analysis shows that, in a one-station, two-state model equipped with a linear cost function, the optimal public design reduces to full information revelation. Moreover, the optimal private design within the proposed cutoff-based class yields significantly better outcomes and, in some cases, can even attain the system optimum. The model is further extended to a corridor setting to examine the impact of different information release schedules. Numerical results show that the private design with sequential release consistently yields the best performance. Under public design, however, the relative effectiveness of different release schedules depends on the spatial configuration of the corridor, among other factors.

Funding: This research is supported by the U.S. National Science Foundation’s Civil Infrastructure System (CIS) Program [Grant CMMI #2225087]. J. Li is supported by the National Natural Science Foundation of China [Grant 72501242].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0017.