Smart electric vehicle charging system

In this work is proposed the design of a system to create and handle Electric Vehicles (EV) charging procedures, based on intelligent process. Due to the electrical power distribution network limitation and absence of smart meter devices, Electric Vehicles charging should be performed in a balanced way, taking into account past experience, weather information based on data mining, and simulation approaches. In order to allow information exchange and to help user mobility, it was also created a mobile application to assist the EV driver on these processes. This proposed Smart Electric Vehicle Charging System uses Vehicle-to-Grid (V2G) technology, in order to connect Electric Vehicles and also renewable energy sources to Smart Grids (SG). This system also explores the new paradigm of Electrical Markets (EM), with deregulation of electricity production and use, in order to obtain the best conditions for commercializing electrical energy.Fundação para a Ciência e a Tecnologia (FCT

Towards a collective knowledge for a smart electric vehicle charging strategy

In this work is proposed the design of a system to create and handle Electric Vehicles (EV) charging procedures, based on intelligent process. Due to the electrical power distribution network limitation and absence of smart meter devices, Electric Vehicles charging should be performed in a balanced way, taking into account past experience (spread in a social network). In order to allow information exchange and to help user mobility, it was also created a mobile application to assist the EV driver on these processes. This proposed Smart Electric Vehicle Charging System uses Vehicle-to-Grid (V2G) technology, in order to connect Electric Vehicles and also renewable energy sources to Smart Grids (SG). This system also explores the new paradigm of Electrical Markets (EM), with deregulation of electricity production and use, in order to obtain the best conditions for commercializing electrical energy.The authors are grateful to the FCT (Fundação para a Ciência e a Tecnologia) and to the MIT-Portugal Program, for funding the Project MIT-PTIEDAM-SMS/00301200

Smart Electric Vehicle Charging System : Controlling Multiple Electrical Vehicle Chargers using OCPP to Limit Electricity Demand

Master's thesis Renewable Energy ENE500 - University of Agder 2017Peak demand is a problem when Electrical Vehicle charging is introduced in the electricity grid. Local limitations like fuses and transformer capacity can rapidly be overloaded if multiple Electrical Vehicles are charging at the same time. This can be solved by shifting these loads in time. This master’s Thesis presents a solution by using the communication protocol OCPP to restrict one or more chargers below a set demand limit. The solution is developed in cooperation with Sharebox [1] and Grønn Kontakt [2], and is in prototype phase at the end of this Master’s Thesis. Testing demonstrates that the prototype can reduce the electricity demand of Charge Points in response to other loads. The solution is adaptable and can with small modifications be improved to limit charging based on overall grid capacity. This improvement will allow demand side management to counter grid overload instead of the common solution to have supply side resolve any grid demands. Key Words: Energy Management, OCPP, EVSE, Demand Limi

Smart charging management for electric vehicle battery chargers

This paper proposes a smart battery charging strategy for Electric Vehicles (EVs) targeting the future smart homes. The proposed strategy consists in regulate the EV battery charging current in function of the total home current, aiming to prevent overcurrent trips in the main switch breaker. Computational and experimental results were obtained under real-time conditions to validate the proposed strategy. For such purpose was adapted a bidirectional EV battery charger prototype to operate in accordance with the aforementioned strategy. The proposed strategy was validated through experimental results obtained both in steady and transient states. The results show the correct operation of the EV battery charger even under heavy load variations.Fundação para a Ciência e Tecnologia within the Project Scope: PEst - OE/EEI/UI0319/201

Real-time information extraction of an electric vehicle

In this paper is presented the development of a project to extract, in real-time, information’s related with an Electric Vehicle (EV). This project was elaborated to extract data from an EV battery charging device developed at the University of Minho, and from an EV prototype, the VEECO (Veículo Eléctrico ECOlógico – Ecologic Electric Vehicle), developed in a cooperation project of ISEL (Lisbon Superior Institute of Engineering) and the Portuguese company VE. The main goal of this project consists in collecting and transmitting the extracted data to inform the EV driver about the performance and the real behavior of the EV. Thereby, it is created an open interface to manage, in real-time, the main data related with the EV, as the batteries SoC (State-of-Charge), the EV speed, and internal temperatures (like the temperatures of the batteries, motor and power electronics inverter), as well as to control the start and stop of the batteries charging process, and to optimize the charging program (to define the best algorithm to preserve the batteries lifespan). This interface also controls the discharging process of the batteries, in order to make possible to deliver back to the electrical power grid part of the stored energy in the batteries, which is defined by the concept Vehicle-to-Grid (V2G). In the paper are presented and described the two main parts of this work: the real-time information extraction system and the charging device.FEDER Funds - Operational Programme for Competitiveness Factors (COMPETE)Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EEA-EEL/104569/2008, MITPT/EDAM-SMS/0030/2008

Electric vehicle assistant based in driver profile

This paper presents the outcomes of a research work consisting in the development of an Electric Vehicle Assistant (EVA), which creates and stores a driver profile where are contained the driving behaviours related with the EV energy consumption, the EV battery charging information, and the performed routes. This is an application for mobile devices that is able to passively track the driver behaviour and to access several information related with the EV in real time. It is also proposed a range prediction approach based on probability to take into account unpredictable effects of personal driving style, traffic or weather.FCT -Fuel Cycle Technologies(SFRH/BD/80155/2011

Status of electric vehicles charging methods

Compared with vehicles powered by fuel, electric vehicles are more efficient in energy saving, emission reduction, and environmental protection. As a result, it is becoming most important with more applications in the transportation sector. As Electric vehicles usage is growing from day to day Electric vehicles (EVs) will become a reality in the future. The time taking the method of charging an EV becomes a major problem to accept the electronic revolution of the automobile industry. In this paper, we have discussed the various charging methods for an Electric vehicle, which also gives us a view of electric vehicle use in today’s world. It gives a brief overview of the present and methods recommended for EV charging

Power quality disturbance mitigation in grid connected photovoltaic distributed generation with plug-in hybrid electric vehicle

In the last twenty years, electric vehicles have gained significant popularity in domestic transportation. The introduction of fast charging technology forecasts increased the use of plug-in hybrid electric vehicle and electric vehicles (PHEVs). Reduced total harmonic distortion (THD) is essential for a distributed power generation system during the electric vehicle (EV) power penetration. This paper develops a combined controller for synchronizing photovoltaic (PV) to the grid and bidirectional power transfer between EVs and the grid. With grid synchronization of PV power generation, this paper uses two control loops. One controls EV battery charging and the other mitigates power quality disturbances. On the grid connected converter, a multicarrier space vector pulse width modulation approach (12-switch, three-phase inverter) is used to mitigate power quality disturbances. A Simulink model for the PV-EV-grid setup has been developed, for evaluating voltage and current THD percentages under linear and non-linear and PHEV load conditions and finding that the THD values are well within the IEEE 519 standards

ELM-ANFIS Based Controller for Plug-In Electric Vehicle to Grid Integration

An Adaptive Neuro Fuzzy Inference System (ANFIS) based Extreme Learning Machine (ELM) theory is utilised in this research work. In particular, the proposed algorithm is applied for designing a controller for electric vehicle to grid (V2G) integration in smart grid scenario. Initially, learning speed and accuracy of this proposed approach are continuously monitored and then, the performance of ELM-ANFIS (e-ANFIS) based controller is examined for its transient response. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Hence, a control practice for their charge and discharge patterns can be easily calculated even with the presence of large numbers of Plug-in Hybrid Electric Vehicles (PHEV). To examine the computational performance and transient response of the e-ANFIS based controller, it is evaluated with the usual ANFIS supported controller. The IEEE 33 bus radial distribution system based approach is implemented to ensure the sturdiness of this prescribed approach