Key Issues for Modelling, Operation, Management and Diagnosis of Lithium Batteries: Current States and Prospects

1 Introduction.With the continuous growth of the global population,the energy demand continues to increase.However,due to the dominance of fossil fuels in global energy and fossil fuels are non-renewable,it has led to the global energy crisis[1].Besides,the use of fossil fuels will generate a mass of air pollutants(e.g.,carbon dioxide,sulfur dioxide,etc.),which will cause serious environmental pollution,climate change[2],etc.To resolve the aforementioned issues,countries around the world have implemented a variety of measures hoping to fundamentally adjust the global energy structure and achieve sustainable development.Thereinto,“Paris Agreement”reached in 2015 under the framework of“United Nations Framework Convention on Climate Change”aims to control the increase in the average temperature of the globe to within 2°C below preindustrial levels,and thereafter to peak global greenhouse gas emissions as soon as possible,continuously decreasing thereafter[3].United Kingdom plans to reduce the average exhaust emissions of“new cars”to approximately 50–70 g/km by 20230,which is roughly half of what it is now[4].In addition,China proposed a plan at“United Nations General Assembly”in 2020 to peak carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060.It is a fact that the whole world is committed to changing the current energy structure,protecting the Earth’s ecology,and achieving global sustainable development[5].

Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries

Lithium-ion batteries are widely recognized as a crucial enabling technology for the advancement of electric vehicles and energy storage systems in the grid.The design of battery state estimation and control algorithms in battery management systems is usually based on battery models,which interpret crucial battery dynamics through the utilization of mathematical functions.Therefore,the investigation of battery dynamics with the purpose of battery system identification has garnered considerable attention in the realm of battery research.Characterization methods in terms of linear and nonlinear response of lithium-ion batteries have emerged as a prominent area of study in this field.This review has undertaken an analysis and discussion of characterization methods,with a particular focus on the motivation of battery system identification.Specifically,this work encompasses the incorporation of frequency domain nonlinear characterization methods and dynamics-based battery electrical models.The aim of this study is to establish a connection between the characterization and identification of battery systems for researchers and engineers specialized in the field of batteries,with the intention of promoting the advancement of efficient battery technology for real-world applications.

Key Optimization Issues for Renewable Energy Systems under Carbon-Peaking and Carbon Neutrality Targets:Current States and Perspectives

1 Introduction The United States,Japan,Canada,the European Union,and other developed countries and regions have all formulated climate strategies and pledged to achieve net-zero CO_(2) emissions by 2050.China,meanwhile,has announced through the“carbon-peaking and carbon neutrality targets”in September 2020 that it aims to achieve“peak carbon use”by 2030 and“carbon neutrality”by 2060[1].According to statistical data from the International Energy Agency(IEA),Fig.1 illustrates the carbon intensity of electricity generation in various regions in the Announced Pledge Scenario(APS)from 2010 to 2040[2].One can easily observe that each region aims to accomplish a sharp decrease in the carbon intensity of electricity generation after 2020.

Assessment on Fault Diagnosis and State Evaluation of New Power Grid:AReview

1 Introduction The proposal of the concept of“New Power System”aims to illustrate the transform direction of the traditional power system,acting as the development core of the future new power grid.To achieve this,the proposed strategic targets of“carbon neutralization and carbon peaking”must be implemented and insisted[1].The core feature of the new power system is that renewable energy plays a leading role and becomes the main source of energy supply,meanwhile,the goal of green energy utilization has also been put forward on the agenda.Green energy utilization includes two aspects,one is the exploitation and promotion of various green energy technologies,and the other is the digitalization of energy management.Under this trend,stochastic and fluctuating energy sources such as wind power and photovoltaic power replace deterministic controllable power sources such as thermal power,bringing challenges to power grid regulation and dispatching,as well as flexible operation.The large-scale integration of renewable energy and increasingly high proportion of power electronic equipment tend to bring about fundamental changes in the operation characteristics,safety control,and production mode of the power system.

Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.

Enhancing hourly heat demand prediction through artificial neural networks:A national level case study

Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption,which is increasingly important.However,conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale,high-resolution,and fast forecasting due to their complexity and the difficulty in obtaining model parameters.This paper presents an artificial neural network(ANN)model to predict hourly heat demand on a national level,which replaces the traditional bottom-up model based on extensive building simulations and computation.The ANN model significantly reduces prediction time and complexity by reducing the number of model input types through feature selection,making the model more realistic by removing non-essential inputs.The improved model can be trained using fewer meteorological data types and insufficient data,while accurately forecasting the hourly heat demand throughout the year within an acceptable error range.The model provides a framework to obtain accurate heat demand predictions for large-scale areas,which can be used as a reference for stakeholders,especially policymakers,to make informed decisions.

A critical survey of technologies of large offshore wind farm integration: summary, advances, and perspectives

Offshore wind farms(OWFs)have received widespread attention for their abundant unexploited wind energy poten-tial and convenient locations conditions.They are rapidly developing towards having large capacity and being located further away from shore.It is thus necessary to explore effective power transmission technologies to connect large OWFs to onshore grids.At present,three types of power transmission technologies have been proposed for large OWF integration.They are:high voltage alternating current(HVAC)transmission,high voltage direct current(HVDC)transmission,and low-frequency alternating current(LFAC)or fractional frequency alternating current transmission.This work undertakes a comprehensive review of grid connection technologies for large OWF integration.Compared with previous reviews,a more exhaustive summary is provided to elaborate HVAC,LFAC,and five HVDC topologies,consisting of line-commutated converter HVDC,voltage source converter HVDC,hybrid-HVDC,diode rectifier-based HVDC,and all DC transmission systems.The fault ride-through technologies of the grid connection schemes are also presented in detail to provide research references and guidelines for researchers.In addition,a comprehensive evalu-ation of the seven grid connection technologies for large OWFs is proposed based on eight specific indicators.Finally,eight conclusions and six perspectives are outlined for future research in integrating large OWFs.

Comprehensive summary of solid oxide fuel cell control:a state-of-the-art review

Hydrogen energy is a promising renewable resource for the sustainable development of society.As a key member of the fuel cell(FC)family,the solid oxide fuel cell(SOFC)has attracted a lot of attention because of characteristics such as having various sources as fuel and high energy conversion efficiency,and being pollution-free.SOFC is a highly coupled,nonlinear,and multivariable complex system,and thus it is very important to design an appropriate control strategy for an SOFC system to ensure its safe,reliable,and efficient operation.This paper undertakes a comprehen-sive review and detailed summary of the state-of-the-art control approaches of SOFC.These approaches are divided into eight categories of control:proportional integral differential(PID),adaptive(APC),robust,model predictive(MPC),fuzzy logic(FLC),fault-tolerant(FTC),intelligent and observer-based.The SOFC control approaches are carefully evalu-ated in terms of objective,design,application/scenario,robustness,complexity,and accuracy.Finally,five perspectives are proposed for future research directions.

Configurations and Control of Traction Motors for Electric Vehicles:A Review

In recent decades,worldwide global warming and reduction in petroleum resources have accelerated researcher’s attention to produce alternative sustainable and environmentally clean transportation systems.Electrification of vehicular technology is capable of curbing the environmental pollution problem in an efficient and effective way,due to high efficiency electric motors,development and advancement in the field of power electronic devices,digital signal processing and advanced control techniques.This article presents a comprehensive review on different configurations/architecture of electric vehicles(EVs)and hybrid electric vehicles(HEVs),traction motors for electric propulsion system and high performance speed sensorless control of traction drive.The basic architecture key components of hybrid vehicle and different power train configurations with respect to applications and limitations are discussed.The integral part of electric propulsion system,traction motor classes for desired operational characteristics and limitations are summarized from a system perspective with the latest improvements.High performance traction motor control techniques are discussed with respect to automotive applications.Finally,speed sensorless control techniques research trends as well as an extensive review on rotor speed estimation techniques for robust and efficient sensorless traction drive control are highlighted.This article provides state of the art key global trends and tradeoff of various technologies with future trends and potential areas of research.

Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction

Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.