Behavior-Driven Planning of Electric Truck Charging Infrastructure for Intercity Operations

Truck stop behavior plays a critical role in supporting powertrain electrification, which is key to reducing emissions in road freight. However, it is still unclear whether these stops meet the charging needs for electric trucks during intercity trips. This study aims to address this issue by analyzing intercity truck travel behavior based on trajectory data to inform battery electric truck (BET) technology design and charging infrastructure planning. The study proposes a data-driven truck trip identification framework based on GPS data to obtain truck stops and truck trip ends. Empirical analysis based on gasoline-enabled truck drivers' travel behaviors in China shows that a 400 km battery range will meet almost 90% of the continuous driving needs of intercity truck trips. It is recommended that en-route charging stations be designed with a charging power of more than 564 kW, in line with the parking time characteristics of 80% of truck stops. The study also identifies potential power supply shortages on the intercity road power grid between 11:00-13:00, 18:00-19:00, and 21:00-1:00. By quantifying the number of facilities needed to provide BET drivers an energy-viable path to complete intercity trips without excessive detour costs, the study found that the travel behavior-based optimization model can reduce the level of truck charging detour to 5% by selecting just a small subset of alternative charging facility locations (55/302). Our study demonstrates that analyzing intercity truck travel behavior can provide critical insights for designing BET technology and charging infrastructure to support the transition to low-emission road freight.