Vehicle Fleet Prediction for V2G System - Based on Left to Right Markov Model

The regulations for internal combustion vehicles, CO2 or NOx emission or noise and so on, are strengthened. Therefore EV (electric vehicle)'s market is expanding. The amount of EV get more, the amount of electric get more and the impact for grid that are voltage fluctuation and frequency fluctuation is concerned. V2G (Vehicle to Grid) can solve this problem, but it has a constraint that EV’s battery can be used during it parked. So as the basic technology, the prediction the vehicles’ state that is driving or parked is important. In this research, machine learning algorithm for predicting vehicle fleet's states is developed. The data for study and test is obtained by person-trip survey. The algorithm is based on left to right Markovmodel. The states are stay or drive from an area to an area. Future state probability is predicted using the latest observed state and state transition probability. As the result, the prediction error of stay is less than the prediction error of drive. Therefore study data and test data are separated into sunny day and rainy day, the prediction error becomes less.