A user-friendly smart charging algorithm based on energy-awareness for different PEV parking scenarios

Abstract

Many studies show that collaborations between renewable energy sources especially PV arrays and plug-in electric vehicles can have a positive impact on the utilization of both emerging technologies. In fact, the intermittent nature of renewable sources and the highdemand for PEV charging will address by this partnership. This paper proposes a user-friendly algorithm for the control of PEV charging and discharging, aiming to satisfy all involved parties including user, parking owner and grid operator by considering the health of batteries and costs. First, the predicted input data are used to obtain the optimal position of PEVs through an extended linear optimization method in different scenarios. In this regard, three scenarios including uncontrolled, unidirectional and bidirectional are used and their impact on the battery health and charging cost is specified. Then, based on energy-awareness, the user selects one of those scenarios arbitrary by comparing their results. Finally, the real-time controller compensates the errors based on a traditional rule-based method. This procedure is repeated upon on arrival of each PEV, and the results are considered as input for the next step.

Predictive smart charging of plug-in electric vehicles for parking fleet scenario with modular converters

“…As mentioned earlier, the main idea of this paper is inspired by the previous work of the authors in [15], which could not be generalized for parking lot scenarios. In this regard, the proposed smart charging algorithm is improved and its flowchart is presented in Fig.…”

“…Also, researchers try to employ model predictive control (MPC) to deal with uncertainties in the desired structure. In one of our recent works in [15], user-friendly smart charging was proposed by considering the charging cost and battery health, in such a way that the user plays a key role in choosing the desired charging scenario for PEV. In another work in [16], a Mamdani-based fuzzy strategy was developed to manage the output power of PEVs.…”

Smart charging analysis for a service provider in mini parking lots by considering the V2V protocol

“…The general structure of the proposed algorithm, which is shown in Fig. 2, is inspired by the previous work of the authors in [4]. In this paper, unlike the previous work in a residential application, the number of vehicles can be more than one and energy exchange can be done between them.…”

“…In other words, by controlling the exchanged power of parked vehicles, the desired objective functions can be pursued. The energy scheduling algorithms can be classified into three main categories according to the type of application: residential [4], [5], workplace [6]- [8], and parking lots [9], [10]. In the residential application, depending on the house or apartment, the number of vehicles is different, the station owner and the vehicle owner are the same, and the parking time is usually long and is from the early hours of the night to morning.…”

PV-Powered Charging Station with Energy Cost Optimization via V2G Services

“…The proposed control strategy is based on a real-time operation to satisfy EV users using PV forecasting and EV charging historical records over four years to predict the EV power profiles. A user-friendly smart charging method, which includes interactions with EV users via an interface, has been developed in [5], where the EV user is a key player in the process of choosing the best scenario among uncoordinated charging, smart charging, and bidirectional smart charging control in a PVCS. The proposed methodology is based on real-time rule-based control and a predictive linear optimization control.…”