An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for charging infrastructure. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and existing charging infrastructure distribution within the entire Beijing area are identified. Then, a charging load forecasting model that can simultaneously simulate the travel chain for aggregate EVs is constructed by considering the occupancy status of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. The results show that the number of charging EVs and the charging power can be reliably predicted with the accuracy of at least 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling.