EV charging behaviour analysis and modelling based on mobile crowdsensing data

With the growing application of electric vehicles (EVs), it is of great significance to have a deep understanding of EV users driving and charging patterns for charging forecasting. However, the rapid growth scale of EV taxis with charging patterns that are closely coupled with human behaviours of temporal-spatial charging choices was not compatible with most previous coordinated strategies. Unlike the majority of existing approaches, a large volume of second-level EV global positioning system (GPS) data was used to study the behaviour patterns of EV users. In practise, a mobile crowd-sensing system that records GPS data and transmits information to the server was deployed in a fleet of electric-taxi cabs in Shenzhen, China, making it possible to record the exact behaviour of each vehicle. Travelling and charging statuses of EVs were recorded and analysed into different characteristics of behaviour for each user. The load forecast methods proved to be more effective with more knowledge of both history and real-time data.