|
The paper proposes using a predictive model to optimize the use of electricity in the V2G
(vehicle to grid) service. The novelty of the mechanism as a kind of model predictive control (MPC) is that
it seeks an effective way of managing electric energy in an Electric Vehicle (EV). Additionally, it proposes
a new method of predicting the electricity consumption which allows the battery of an electric vehicle to
reconcile two sides: both the system’s and the user’s demand will be met at the same time. The model allows
for very precise determination of the vehicle’s demand for the energy related to the progressive movement,
taking into account the parameters characteristic of a given vehicle model, its suspension structure and
aerodynamics. In addition, the machine learning algorithm was proposed for the prediction model as a hybrid
(offline and online) of supervised learning. As the first part of the research, by using Matlab/Simulink/dSpace
software, a prediction of EV energy consumption was created on a selected route at different times of the
day (offline data matrix). At the same time, the simulated route was travelled by a BMW i3 EV (online data
matrix). Based on the developed machine learning algorithm the results of the electric energy consumption
were compared. The research confirms that if the correct mechanism for prediction of energy consumption
by the EV is used, it is possible to define the amount of energy needed for a V2G service. The measurement
error was obtained at 0.5%. The added value is setting up the EV energy security of customers after the
|
