State of charge and health estimation of batteries for electric vehicles applications:key issues and challenges

Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.

State coordinated voltage control in an active distribution network with on-load tap changers and photovoltaic systems

Decreasing costs and favorable policies have resulted in increased penetration of solar photovoltaic(PV)power generation in distribution networks.As the PV systems penetration is likely to increase in the future,utilizing the reactive power capability of PV inverters to mitigate voltage deviations is being promoted.In recent years,droop control of inverter-based distributed energy resources has emerged as an essential tool for use in this study.The participation of PV systems in voltage regulation and its coordination with existing controllers,such as on-load tap changers,is paramount for controlling the voltage within specified limits.In this work,control strategies are presented that can be coordinated with the existing controls in a distributed manner.The effectiveness of the proposed method was demonstrated through simulation results on a distribution system.

Resiliency oriented control of a smart microgrid with photovoltaic modules

The resiliency of a standalone microgrid is of considerable issue because the available regulation measures and capabilities are limited.Given this background,this paper presented a new mathematical model for a detailed photovoltaic(PV)module and the application of new control techniques for efficient energy extraction.The PV module employs a single-stage conversion method to integrate it with the utility grid.For extraction the maximum power from PV and integrate it to power grid,a three-phase voltage source converter is used.For obtaining the maximum power at a particular irradiance a maximum power point tracking(MPPT)scheme is used.The fuzzy logic control and adaptive network-based fuzzy inference system are proposed for direct current(DC)link voltage control.The proposed model and control scheme are validated through a comparison with the standard power-voltage and current-voltage charts for a PV module.Simulation results demonstrate that the system stability can be maintained with the power grid and in the island mode,in contrast with the MPPT.

Technologies and Practical Implementations of Air-conditioner Based Demand Response

Nowadays,the most notable uncertainty for an electricity utility lies in the electrical demand of end-users.Demand response(DR)has acquired considerable attention due to uncertain generation outputs from intermittent renewable energy sources and advancements of smart grid technologies.The percentage of the air-conditioner(AC)load over the total load demand in a building is usually very high.Therefore,controlling the power demand of ACs is one of significant measures for implementing DR.In this paper,the increasing development of ACs,and their impacts on power demand are firstly introduced,with an overview of possible DR programs.Then,a comprehensive review and discussion on control techniques and DR programs for ACs to manage electricity utilization in residential and commercial energy sectors are carried out.Next,comparative analysis among various programs and projects utilized in different countries for optimizing electricity consumption by ACs is presented.Finally,the conclusions along with future recommendations and challenges for optimal employment of ACs are presented in the perspective of power systems.

Optimal Pricing Strategy for Data Center Considering Demand Response and Renewable Energy Source Accommodation

With the continuous development of information technology,data centers(DCs)consume significant and evergrowing amounts of electrical energy.Renewable energy sources(RESs)can act as clean solutions to meet this requirement without polluting the environment.Each DC serves numerous users for their data service demands,which are regarded as flexible loads.In this paper,the willingness to pay and time sensitivities of DC users are firstly explored,and the user-side demand response is then devised to improve the overall benefits of DC operation.Then,a Stackelberg game between a DC and its users is proposed.The upper-level model aims to maximize the profit of the DC,in which the time-varying pricing of data services is optimized,and the lower-level model addresses user’s optimal decisions for using data services while balancing their time and cost requirements.The original bi-level optimization problem is then transformed into a single-level problem using the Karush-Kuhn-Tucker optimality conditions and strong duality theory,which enables the problem to be solved efficiently.Finally,case studies are conducted to demonstrate the feasibility and effectiveness of the proposed method,as well as the effects of the time-varying data service price mechanism on the RES accommodation.

Predictive Control Based on Ranking Multi-objective Optimization Approaches for a quasi-Z Source Inverter

In power converter control,predictive control has several merits,such as simple concept and fast response.However,the necessity to use the weighting factor inside the cost function makes the control design complex in the case of regulating multivariables where the value of the weighting factor is obtained by a nontrivial process.Also,it primarily depends on the system parameters and operating points of the control system.This paper aims to enhance the model predictive algorithm of the singlestage topology of a quasi-Z Source Inverter(qZSI).The concept of a multi-objective optimization approach is used in addition to the sub-cost function definition to remove the weighting factors.By using the sub-cost function definition,the inductor current is pushed away from the main loop of the predictive algorithm.Thus,no weighting factor is needed to manage the priority of the inductor current.The other two control targets,which are the capacitor voltage and load currents,will be controlled by the multi-objective optimization approach without using any weighting factors.A detailed theoretical analysis of the proposed technique will be given and validated based on simulation results.