A review on charging behavior of electric vehicles: data, model, and control

Abstract

The adoption and usage of electric vehicles (EVs) have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution. As a part of the smart grid, EVs can provide valuable ancillary services beyond consumers of electricity. However, EVs are gradually considered as nonnegligible loads due to their increasing penetration, which may result in negative effects such as voltage deviations, lines saturation, and power losses. Relationship and interaction among EVs, charging stations, and micro grid have to be considered in the next generation of smart grid. Therefore, the topic of smart charging has been the focus of many works where a wide range of control methods have been developed. As one of the bases of simulation, the EV charging behavior and characteristics have also become the focus of many studies. In this work, we review the charging behavior of EVs from the aspects of data, model, and control. We provide the links for most of the data sets reviewed in this work, based on which interested researchers can easily access these data for further investigation.

Matching Uncertain Renewable Supply with Electric Vehicle Charging Demand—A Bi-Level Event-Based Optimization Method

“…Detailed review of related datasets and optimization methods can be found in Ref. [18]. Among these works, the idea of EV Aggregators (EVAs) is an effective way to schedule EVs fleet which can save computation and communication burden [19] .…”

“…An event e is defined as a set of state transitions that have certain common properties [20] and EBO is mathematically equivalent to the partially observable MDP [21] . Generally, the event space is much smaller compared with the original MDP and many works applied EBO to address the EV charging problem [18,22] . Note that these works have not explored the fact that some events only capture local state changes, while some events capture global state changes.…”

“…Equation ( 15) ensures that the charging power (combining the renewable energy and the electricity) is sufficient for the set of EVs that must be charged, i.e., (19) where N full t;k denotes the number of EVs fully charged at EVA k at time step t . The constraint Formula (18) guarantees that the number of EVs that need to be charged is always larger than the number of EVs that is already fully charged. The constraint Formula (19) means that the upper bound of the number of EVs that must be charged is the number of EVs that need to be charged.…”

Electric Vehicles Charging Management for Real-Time Pricing Considering the Preferences of Individual Vehicles

“…Many control schemes have been proposed to overcome the negative impacts of EV charging. A control strategy using data-driven models has been implemented to relax power system components and prevent excessive loading due to EV charging demand [27][28][29]. Demand side management (DSM) works to guarantee EVs' demand satisfaction in all grid situations and certifies grid stability [30][31][32][33][34].…”

Analysis of Technical Capabilities, Methodology and Test Results of a Light-Commercial Vehicle Conversion to Battery Electric Powertrain

“…Additionally, to meet the following criteria: Provide power to other on-board equipment and subsystems, e.g., lights, cabin heating, ventilation and air conditioning (HVAC) systems, radio, power drives, electronic control units, etc. [4,5,18].…”