Multi‐fault synergistic diagnosis of battery systems based on the modified multi‐scale entropy

Faults of lithium batteries in their early stage in electric vehicles (EVs) are usually undetectable, and their characteristics are difficult to be extracted by conventional methods. This paper presents a novel synergistic diagnosis scheme for multiple battery faults using the modified multi‐scale entropy (MMSE). The proposed MMSE can effectively extract the multi‐scale features of complex battery signals in the early stages of battery faults as well as overcome the shortage of the coarse‐grained mode in the standard multi‐scale entropy. The simulation results on experimental data and the real‐world operational vehicles show that the proposed method can effectively detect and locate multiple battery faults/abnormities before they trigger the alarm thresholds. The defined sensitivity factor can implement real‐time evaluation on abnormities with high efficiency and stability, and the developed variable‐calculation‐window diagnosis scheme can synchronously detect and locate different fault types in real time. Furthermore, feasibility, stability, reliability, versatility, robustness, and practicality of the proposed method are separately verified using multiple sets of real‐world operation data. More importantly, the proposed method also provides feasibility to effectively prevent battery thermal runaway caused by multiple battery abnormities/faults. The applications of multi‐scale entropy theory is the first of its kind to battery fault diagnosis on the real‐world operational vehicles.     

A new IoT‐based smart energy meter for smart grids

The significant increase in energy consumption by the growth of the population or by the use of new equipment has brought big challenges to the energy security as well as the environment. There is a need that consumers can track their daily use and understand consumption standards for better organizing themselves to obtain financial and energetic efficiency. With the improvement of smart networks technology for better energy supply, a smart meter is not just a simple measurement gadget anymore, but it has additional functions including smart equipment control, bidirectional communication that allows integration of users and networks, and other functionalities. Smart meters are the most fundamental components in smart power grids. Besides, the meters used with a management system can be utilized for monitoring and controlling home appliances and other gadgets according to the users' need. A solution of an integrated and single system should be more efficient and economical. Smart measurement systems allow monitoring the energy consumption of the final consumers while providing useful information about the energy quality. The information provided by these systems is used by the operators to enhance the energy supply, and different techniques can be also applied for this end, such as charge scheduling, management from the demand side, and non‐intrusive load monitoring. The Internet of Things (IoT) is becoming a great ally in the management of smart distribution and energy consumption in smart systems scenarios. To address these issues, this paper proposes and demonstrates a new smart energy meter following an IoT approach and its associated costs and benefits. The developed device incorporates several communication interfaces. In order to easily integrate with any monitoring software solution, the meter has a multi‐protocol connection. Finally, the provided solution is validated and demonstrated in real‐life environments and it is also under use.     

Graphene‐based materials for flexible electrochemical energy storage

Sustainable development of renewable energy sources is one of the most important themes that humanity faces in this century. Wide use of renewable energy sources will require a drastically increased ability to store electrical energy. Electrochemical energy storage devices are expected to play a key role. With the increased demand in flexible energy resource for wearable electronic devices, great efforts have been devoted to developing high‐quality flexible electrodes for advanced energy storage and conversion systems. Because of its high specific surface area, good chemical stability, high mechanical flexibility, and outstanding electrical properties, graphene, a special allotrope of carbon with two‐dimensional mono‐layered network of sp² hybridized carbon, have been showing great potential in next‐generation energy conversion and storage devices. This review presents the latest advances on the flexible graphene‐based materials for the most vigorous electrochemical energy storage devices, that is, supercapacitors and lithium‐ion batteries. Copyright © 2014 John Wiley & Sons, Ltd.     

Multi‐criteria algorithm‐based methodology used to select suitable domes for compressed air energy storage

Storing energy allows both the efficiency and availability of renewable energy to be increased, thus dissociating actual from expected generation and from consumption demands. Compressed air energy storage (hereinafter ‘CAES’) enables the efficient and cost‐effective storage of large amounts of energy, achieving a capacity of over 100 MWh. There are several geological structures that can be used as CAES, among which the use and construction of salt domes are particularly noteworthy. However, there is a high exploration risk associated with subsurface exploration. To this end, it is advisable to establish a detailed schedule to select and characterize structures, with the purpose of minimizing the aforementioned risk. Multi‐criteria algorithms can be used to establish a hierarchy of the alternatives and to identify the structures with the greatest potential with an objective approach. The analytic hierarchy process method is used in this paper as the selection algorithm, which is based on identifying and assessing criteria and weighting each criterion. In accordance with the analytic hierarchy process method, the goal was divided into a series of different level criteria, defining a breakdown structure of the problem to select salt domes. This paper defines a structure hierarchization method that allows the objective establishment of the areas with the highest potential for CAES, considering both technical and socioeconomic factors. Therefore, a supporting decision‐making method may be established to reduce the exploration risk associated with underground structures. Copyright © 2017 John Wiley & Sons, Ltd.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.     

Optimization‐based power management of a wind farm with battery storage

This paper presents an optimization‐based control strategy for the power management of a wind farm with battery storage. The strategy seeks to minimize the error between the power delivered by the wind farm with battery storage and the power demand from an operator. In addition, the strategy attempts to maximize battery life. The control strategy has two main stages. The first stage produces a family of control solutions that minimize the power error subject to the battery constraints over an optimization horizon. These solutions are parameterized by a given value for the state of charge at the end of the optimization horizon. The second stage screens the family of control solutions to select one attaining an optimal balance between power error and battery life. The battery life model used in this stage is a weighted Amp‐hour throughput model. The control strategy is modular, allowing for more sophisticated optimization models in the first stage or more elaborate battery life models in the second stage. The strategy is implemented in real time in the framework of model predictive control. Copyright © 2012 John Wiley & Sons, Ltd.     

Battery control for leveling the amount of electricity purchase in smart‐energy houses

An optimal operation method in smart‐energy houses with photovoltaics (PV) and a storage battery was investigated in a trial production system. In this method, the inverse current of the PV output is not conveyed to the commercial electricity system as operation conditions. Instead, the excess of the consumed PV power is applied to leveling the electricity purchase by appropriately charging and discharging the storage battery. To validate the proposed system, a lithium battery (4 kWh) and PV cell (3 kW) used in average individual houses was installed in a smart‐energy house in a local city (Kitami) in Japan. Another example was introduced into a wider area (Hokkaido, Japan). Accounting for the error between the weather forecast and actual solar radiation, the trial production system reduced the range in the electricity purchase amount by 75.0%, 77.0%, and 73.0% on a representative day in January, April, and July, respectively. The accuracy of the reduction effect in the trial production system, obtained in the proposed optimization analysis, ranged from 1.9% to 7.2%. Moreover, the CO₂ emissions were reduced by 1.990 kg‐CO₂/(Day‐House) in January, 2.910 kg‐CO₂/(Day‐House) in April, and 2.210 kg‐CO₂/(Day‐House) in July.     

An energy storage system for smart coal mine emergency power supply

为满足煤炭行业和煤矿企业对于供电可靠性日益增长的需求,同时探索兆瓦级储能系统在工业用户侧的实用化解决方案,本项目在内蒙古乌海平沟煤矿设计建造了基于铅酸电池和磷酸铁锂电池储能技术的矿用兆瓦级智能应急电源。系统主要功能为：在电网正常供电时,替代传统的油浸电容器进行无功补偿;在电网出现供电故障时,为煤矿的特别重要负荷提供至少30 min的连续可靠供电。除此外,系统还可根据用户需求执行包括削峰填谷、分布式新能源发电波动平抑在内的多种功能。为保证应急电源系统的安全性、可靠性和使用寿命,本工作在进行设计时着重考虑了蓄电池的选型、容量配比、成组设计以及储能变流系统（PCS）的电路拓扑设计和电池维护高级智能控制策略,旨在探索和实用。     

Load‐frequency control of an interconnected hydro‐thermal power system with new area control error considering battery energy storage facility

This paper deals with load‐frequency control of an interconnected hydro‐thermal system considering battery energy storage (BES) system. A new area control error (ACEN) based on tie‐power deviation, frequency deviation, time error and inadvertent interchange (unscheduled energy transfer) is used for the control of the BES system. Time domain simulations are used to study the performance of the power system and the BES system. Results reveal that BES meets sudden requirements of real power load and is very effective in reducing the peak deviations of frequencies, tie‐power, time errors and inadvertent interchange accumulations. Copyright © 2000 John Wiley & Sons, Ltd.     

An investigation of optimum PV and wind energy system capacities for alternate short and long‐term energy storage sizing methodologies

The goal of this study is to find the optimal sizes of renewable energy systems (RES) based on photovoltaic (PV) and/or wind systems for three energy storage system (ESS) scenarios in a micro‐grid; (1) with pumped hydro storage (PHS) as a long‐term ESS, (2) with batteries as a short‐term ESS, and (3) without ESS. The PV and wind sizes are optimally determined to accomplish the maximum annual RES fraction (F_RES ) with electricity cost lower than or equal to the utility tariff. Furthermore, the effect of the use of battery and PHS on the electricity cost and F_RES are studied. A university campus on a Mediterranean island is selected as a case study. The results show that PV‐wind hybrid system of 8 MW wind and 4.2 MW PV with 89.5 MWh PHS has the highest F_RES of 88.0%, and the highest demand supply fraction as 42.6%. Moreover, the results indicate that the economic and technical parameters of RESs are affected significantly by the use of ESSs depending on the type and the capacity of both the RES and the ESS.     

A review of electrical energy storage technologies for renewable power generation and smart grids

储能技术是突破可再生能源大规模开发利用瓶颈的关键技术,是智能电网的必要组成部分.在储能市场商业化雏形阶段,系统性的比较分析各类储能技术的性能特点,为未来市场发展提供筛选技术路线的框架基础至关重要.本文阐述了储能技术在可再生能源发电和智能电网中的作用,对物理储能(抽水蓄能,压缩空气储能,飞轮储能),电化学储能(二次电池,液流电池),其它化学储能(氢能,合成天然气)等储能技术进行了系统的比较与分析,最后提出储能技术的发展趋势.     

Multi‐objective design optimization for mini‐channel cooling battery thermal management system in an electric vehicle

Lithium‐ion battery packs have been generally used as the power source for electric vehicles. Heat generated during discharge and limited space in the battery pack may bring safety issues and negative effect on the battery pack. Battery thermal management system is indispensable since it can effectively moderate the temperature rise by using a simple system, thereby improving the safety of battery packs. However, the comprehensive investigation on the optimal design of battery thermal management system with liquid cooling is still rare. This article develops a comprehensive methodology to design an efficient mini‐channel cooling system, which comprises thermodynamics, fluid dynamics, and structural analysis. The developed methodology mainly contains four steps: the design of the mini‐channel cooling system and computational fluid dynamics analysis, the design of experiments and selection of surrogate models, formulation of optimization model, and multi‐objective optimization for selection of the optimum scheme for mini‐channel cooling battery thermal management system. The findings in the study display that the temperature difference decreases from 8.0878 to 7.6267 K by 5.70%, the standard temperature deviation decreases from 2.1346 to 2.1172 K by 0.82%, and the pressure drop decreases from 302.14 to 167.60 Pa by 44.53%. The developed methodology could be extended for industrial battery pack design process to enhance cooling effect thermal performance and decrease power consumption.     

A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems

Demand‐side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low‐voltage metering hardware that uses an ARM Cortex M4 and real‐time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real‐time operating system provided by Texas Instruments (called TI‐RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are significant in the analysis of power quality in a smart grid scenario, as the smart meter offers low cost and high accuracy in both detecting (an accuracy rate above 90%) and classifying (an average accuracy rate above 94%) disturbances.     

A market‐oriented wind power dispatch strategy using adaptive price thresholds and battery energy storage

In this paper, an adaptive dispatch strategy is presented to maximize the revenue for grid‐tied wind power plant coupled with a battery energy storage system (BESS). The proposed idea is mainly based on time‐varying market‐price thresholds, which are varied according to the proposed algorithm in an adaptive manner. The variable nature of wind power and market price signals leads to the idea of storing energy at low price periods and consequently selling it at high prices. In fact, the wind farm operators can take advantage of the price variability to earn additional income and to maximize the operational profit based on the choice of best price thresholds at each instant of time. This research study proposes an efficient strategy for intermittent power dispatch along with the optimal operation of a BESS in the presence of physical limits and constraints. The strategy is tested and validated with different BESSs, and the percentage improvement of income is calculated. The simulation results, based on actual wind farm and market‐price data, depict the proficiency of the proposed methodology over standard linear programming methods.     

A graphical model for evaluating the status of series‐connected lithium‐ion battery pack

State evaluation of battery pack is essential for battery management but laborious when dealing with massive information of cells within the pack. A graphical model for evaluating the status of series‐connected Li‐ion battery pack is established to release the burden. The model is founded by a 2D diagram, with the electric quantity “E” and the capacity “Q” as its axes, therefore called by the “E‐Q diagram.” The new graphical diagram presents the dynamics of cell variations in a linear way, thereby benefiting the design and management of battery pack, including (1) quantifying the cell variations by region, (2) illustrating the evolution of cell variations during aging, (3) guiding the estimation of pack states considering algorithm error in cell states, and (4) solving the balancing problem. The experimental results conform to the theoretical analysis, indicating that the E‐Q diagram will be pervasively applied in the design and management of series‐connected battery pack. Moreover, the E‐Q diagram is suitable for education on the basics of a battery pack, because it is a graphical model.     

Organizing prosumers into electricity trading communities: Costs to attain electricity transfer limitations and self‐sufficiency goals

Among household electricity end users, there is growing interest in local renewable electricity generation and energy independence. Community‐based and neighborhood energy projects, where consumers and prosumers of electricity trade their energy locally in a peer‐to‐peer system, have started to emerge in different parts of the world. This study investigates and compares the costs incurred by individual households and households organized in electricity trading communities in seeking to attain greater independence from the centralized electricity system. This independence is investigated with respect to: (i) the potential to reduce the electricity transfer capacity to and from the centralized system and (ii) the potential to increase self‐sufficiency. An optimization model is designed to analyze the investment and operation of residential photovoltaic battery systems. The model is then applied to different cases in a region of southern Sweden for year 2030. Utilizing measured electricity demand data for Swedish households, we show that with a reduced electricity transfer capacity to the centralized system, already a community of five residential prosumers can supply the household demand at lower cost than can prosumers acting individually. Grouping of residential prosumers in an electricity trading community confers greater benefits under conditions with a reduced electricity transfer capacity than when the goal is to become electricity self‐sufficient. It is important to consider the local utilization of photovoltaic‐generated electricity and its effect on the net trading pattern (to and from the centralized system) when discussing the impact on the electricity system of a high percentage of prosumers.     

A cryogen‐based peak‐shaving technology: systematic approach and techno‐economic analysis

A peak‐shaving technology is recently proposed, which integrates peak‐electricity generation, cryogenic energy storage and CO₂ capture. In such a technology, off‐peak electricity is used to produce liquid nitrogen and oxygen in an air separation and liquefaction unit. At peak hours, natural gas (or alternative gases, e.g. from gasification of coal) is burned by oxygen from the air separation unit (oxy‐fuel combustion) to generate electricity. CO₂ produced is captured in the form of dry ice. Liquid nitrogen produced in the air separation plant not only serves as an energy storage medium but also supplies the low‐grade cold energy for CO₂ separation. In addition, waste heat from the tail gas can be used to superheat nitrogen in the expansion process to further increase the system efficiency. This article reports a systematic approach, with an aim to provide technical information for the system design. Three potential blending gases (helium, oxygen and CO₂) are considered not only for assessing thermodynamic performance but also for techno‐economic analysis. The peak‐shaving systems are also compared with natural gas combined cycle and an oxy–natural gas combined cycle in terms of capital cost and peak electricity production cost. Copyright © 2011 John Wiley & Sons, Ltd.     

The effects of vehicle‐to‐grid systems on wind power integration

Renewable energy portfolio standards have created a large increase in the amount of renewable electricity production, and one technology that has benefited greatly from these standards is wind power. The uncertainty inherent in wind electricity production dictates that additional amounts of conventional generation resources be kept in reserve, should wind electricity output suddenly dip. The introduction of plug‐in hybrid electric vehicles into the transportation fleet presents an possible solution to this problem through the concept of vehicle‐to‐grid power. The ability of vehicle‐to‐grid power systems to help solve the variability and uncertainty issuess in systems with large amounts of wind power capacity is examined through a multiparadigm simulation model. The problem is examined from the perspectives of three different stakeholders: policy makers, the electricity system operator and plug‐in hybrid electric vehicle owners. Additionally, a preliminary economic analysis of the technology is performed, and a comparison made with generation technologies that perform similar functions. Copyright © 2011 John Wiley & Sons, Ltd.     

Introducing the energy efficiency map of lithium‐ion batteries

The charge, discharge, and total energy efficiencies of lithium‐ion batteries (LIBs) are formulated based on the irreversible heat generated in LIBs, and the basics of the energy efficiency map of these batteries are established. This map consists of several constant energy efficiency curves in a graph, where the x‐axis is the battery capacity and the y‐axis is the battery charge/discharge rate (C‐rate). In order to introduce the energy efficiency map, the efficiency maps of typical LIB families with graphite/LiCoO₂, graphite/LiFePO₄, and graphite/LiMn₂O₄ anode/cathode are generated and illustrated in this paper. The methods of usage and applications of the developed efficiency map are also described. To show the application of the efficiency map, the effects of fast charging, nominal capacity, and chemistry of typical LIB families on their energy efficiency are studied using the generated maps. It is shown how energy saving can be achieved via energy efficiency maps. Overall, the energy efficiency map is introduced as a useful tool for engineers and researchers to choose LIBs with higher energy efficiency for any targeted applications. The developed map can be also used by energy systems designers to obtain accurate efficiency of LIBs when they incorporate these batteries into their energy systems.     

BEBB based design for underground coal mine emergency power application

鉴于目前国内只有48 V锂离子电池组能够通过安全标准认证的现实情况,标准电池储能系统不能被直接用来为几百伏额定电压的电机/泵类电器负荷提供应急电源。本文开发了一种名为电池电子标准单元的模块化设计系统,该系统可以将48 V的电池/变频器模块以即插即用的形式根据负荷的实际需要整合到一起。该系统提供了一种新颖的、集成的电池及电力电子系统控制构架,该构架能够在不使用额外的电池管理系统（BMS）的情况下构建稳定可靠的储能系统。基于上述概念,一种新颖的、被定义为能源计算机的分布式能源系统构架被本文提出并与普通个人计算机进行了类比说明。