Smart Electric Vehicle Charging via Adjustable Real-Time Charging Rates

This paper presents a plug-in electric vehicle (PEV) charging control algorithm, Adjustable Real-Time Valley Filling (ARVF), to improve PEV charging and minimize adverse effects from uncontrolled PEV charging on the grid. ARVF operates in real time, adjusts to sudden deviations between forecasted and actual baseloads, and uses fuzzy logic to deliver variable charging rates between 1.9 and 7.2 kW. Fuzzy logic is selected for this application because it can optimize nonlinear systems, operate in real time, scale efficiently, and be computationally fast, making ARVF a robust algorithm for real-world applications. In addition, this study proves that when the forecasted and actual baseload vary by more than 20%, its real-time capability is more advantageous than algorithms that use optimization techniques on predicted baseload data.

A holistic review on advanced bi-directional EV charging control algorithms

The rapid growth of electric vehicles (EVs) has promised a next-generation transportation system with reduced carbon emission. The fast development of EVs and charging facilities is driving the evolution of Internet of Vehicles (IoV) to Internet of Electric Vehicles (IoEV). IoEV benefits from both smart grid and Internet of Things (IoT) technologies which provide advanced bi-directional charging services and real-time data processing capability, respectively. The major design challenges of the IoEV charging control lie in the randomness of charging events and the mobility of EVs. In this article, we present a holistic review on advanced bi-directional EV charging control algorithms. For Grid-to-Vehicle (G2V), we introduce the charging control problem in two scenarios: 1) Operation of a single charging station and 2) Operation of multiple charging stations in coupled transportation and power networks. For Vehicle-to-Grid (V2G), we discuss how EVs can perform energy trading in the electricity market and provide ancillary services to the power grid. Besides, a case study is provided to illustrate the economic benefit of the joint optimization of routing and charging scheduling of multiple EVs in the IoEV. Last but not the least, we will highlight some open problems and future research directions of charging scheduling problems for IoEVs.

Automatic Design of Battery Charging System Power Supply from Photovoltaic Sources Base on Voltage

Short charging times are desirable from a battery powered system. However, the short charging time must also be considered the reliability of the system. Where the short charging time does not cause damage to the control system and battery. The battery has an important role as a source of power supply when the sun is not bright. By minimizing battery charging time, the battery can be maximally utilized as a power store. So the minimum charging time is obtained, but with maximum storage power. We present battery charging control method and auto switch off on this system. The controller is based on a constant voltage (CV) charge control scheme. In order to keep the parameters constant, this research prototype uses a DC-DC controller. The experimental results show that, the new controller charging period is significantly reduced. Moreover, the proposed controller has high accuracy and minimized battery overcharging.

A Multi-Particle Physics-Based Model of a Lithium-Ion Battery for Fast-Charging Control Application

The charging safety of electric vehicles is an area of focus in the electric automobile industry. For the purpose of ensuring safety, charging electric vehicles as soon as possible is a goal pursued by the public. In order to ensure the safety of electric vehicles during fast charging and to reduce the cycle life decay of the battery, a simplified multi-particle lithium-ion battery model is proposed, based on the pseudo two-dimensional (P2D) model. The model was developed by considering heterogeneous electrochemical reactions in the negative electrode area. The Butler–Volmer (BV) kinetic equation and the distribution of the pore wall flux in the negative electrode is approximated by the quasi-linear approximation method. Furthermore, this paper also analyzes the conditions of lithium precipitation from the negative electrode of a lithium-ion battery in the case of high charging rates, which has a certain reference significance for fast-charging control applications. The experimental and simulation results show that the model has a high simulation accuracy and can reflect the heterogeneity of electrochemical reactions in the negative electrode of the battery. The model can be adapted to fast-charging control applications.