The V2G Process With the Predictive Model

Abstract

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 V2G service and a correct WIN-WIN relation between the Low Voltage grid and EV customers' needs.

The Role of the Electric Vehicle in Smart Homes: Assessment and Future Perspectives

“…As investigated and demonstrated in several published studies, as in [17], [18], [19], [19], [20], [21], [22], and [23], this option of bidirectional power interface has an extremely vital impact, permitting, for example, to control the EV to overcome issues of efficiency and power quality in the power grid. In this standpoint, with the consent of the EV driver (e.g., guaranteeing advantageous tariffs for programs of power management sustained by the G2V/V2G modes, and knowing that the EV battery can be degraded when subjected to additional charge/discharge cycles), the EV can be controlled by an algorithm of power management of the smart home or smart grid, which outlines the programming modes for charging (G2V) and discharging (V2G) processes [24].…”

SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning

“…Electric vehicles (EVs) have become popular due to several environmental and economical advantages compared to fossil fuel cars such as reducing pollution, greenhouse gas emission, and operation cost [1]. In modern transportation systems, EVs are the preferred choice due to their energy and environmental sustainability, although they often fail to find proper charging stations due to the limited infrastructure of Smart Grid (SG) and unbalance demand of charging.…”

“…erefore, the efficient management of the EVs charging/discharging to reduce cost has become challenging due to random EV's arrival and departure times, randomness of the EVs' utility, and dynamic price of energy. In V2G, EVs can be used for excess energy storage from the SG to balance energy demand, smooth demand peak, and eliminate distribution losses [1].…”

“…; at the same time, there are disadvantages like adverse battery effects, new infrastructure, new trading mechanism, and security from cyberattacks, replay attacks, data manipulation attacks, etc. For example, Bartłomiej et al [1] presented the effectiveness of the V2G process using predictive model [9,10]. Moreover, the various ML-and DL-based approaches exist, but they have various challenges; for example, ML/DL models are trained on the training data and then tested over the test dataset.…”

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

“…The authors in [ 18 ] propose using the BLDC motor thermal model to optimize the trajectory of the industrial robot movements, taking into account thermal constraints. Moreover, based on the research conducted by the authors in [ 19 ], it can be concluded that thermal modeling of a traction motor with permanent magnets will have a significant impact on the optimization of V2G (vehicle to grid) systems, because the amount of energy consumed by an electric vehicle is dependent on thermal losses of its motor.…”