Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

The electric vehicle (EV) sales have significantly grown over the years to fulfill growing demands for economic travel and greenhouse gas mitigation1. However, the surge in the number of EVs has led to charging anxiety as users struggle to find an available charging station before running out of electricity, resulting in longer reserved and waiting time2. Moreover, severe mobility restrictions caused by infectious diseases, such as COVID-19, have greatly affected people’s travel behavior3,4 and hindered their willingness to use EVs, given that charging in public spaces consumes time and increases the risk of contracting the virus5. This implies that in the post-pandemic era, where individuals coexist with the virus, the interplay between the two important trends, namely vehicle electrification and mobility restrictions, can extensively affect people’s daily commuting by using EVs6,7. Hence, it is vital to investigate the interaction between vehicle electrification and mobility restrictions, which is unexplored in the current literature. Since official communications regarding confirmed COVID-19 cases can influence people’s travel behavior8,9 and EV charging can directly reflect users' propensity to use EVs, quantifying vehicle electrification through EV charging data is an appropriate approach to unravel these interactions. In summary, this study aims to quantify and characterize the interaction between the two trends mentioned above, seeking to understand the diverse influences of confirmed cases and associated factors on EV charging behavior, especially when significant interactions are observed.