Electric Vehicle Trip Information Inference Based on Time-Series Residential Electricity Consumption

Abstract

Pure electric vehicles (EVs) become more and more popular in current automotive markets. With wide application of EVs in the market, more EVs will be charged in residential homes. An advanced metering infrastructure measures real-time electricity consumption of individual residential homes. However, the advanced metering infrastructure is exposed to attacks given the fact that its components (e.g., smart meters) are located at public places. Under this situation, electricity consumption information of an individual residential home with an EV can be easily stolen. In this paper, we propose an EV Trip Information Inference System (TIIS) to infer EV trip information (origin-destination of each driving trip in a day) of a residential home based on its real-time electricity consumption measurements in a day. To the best of our knowledge, this is the first driving privacy relevant work caused only by residential electricity consumption data. For a residential home with an EV, TIIS derives the relationship between trip numbers in a day and charging-starting time, and the relationship between total driving distance and total charging energy. To derive the EV trip information of a residential home, TIIS firstly extracts an EV charging profile from its electricity consumption data and identifies EV model type and trip numbers as well as total driving distance based on EV charging profiles. Then, TIIS infers EV trip information based on the identified information. We used electricity consumption data and EV driving data to evaluate inference performance of TIIS. The experimental results demonstrate TIIS has as high as 81% trip inference accuracy and works on residential homes with different EV model types.