Sensing as the key to the safety and sustainability of new energy storage devices

New energy storage devices such as batteries and supercapacitors are widely used in various felds because of their irreplaceable excellent characteristics. Because there are relatively few monitoring parameters and limited understanding of their operation, they present problems in accurately predicting their state and controlling operation, such as state of charge, state of health, and early failure indicators. Poor monitoring can seriously afect the performance of energy storage devices. Therefore, to maximize the efciency of new energy storage devices without damaging the equipment, it is important to make full use of sensing systems to accurately monitor important parameters such as voltage, current, temperature, and strain. These are highly related to their states. Hence, this paper reviews the sensing methods and divides them into two categories: embedded and non-embedded sensors. A variety of measurement methods used to measure the above parameters of various new energy storage devices such as batteries and supercapacitors are systematically summarized. The methods with diferent innovative points are listed, their advantages and disadvantages are summarized, and the application of optical fber sensors is emphasized. Finally, the challenges and prospects for these studies are described. The intent is to encourage researchers in relevant felds to study the early warning of safety accidents from the root causes.     

考虑电池储能老化和需求侧响应的交直流混合配电网动态重构方法

现有的电池储能模型一方面认为电池的充放电功率和效率为固定不变的,另一方面有关电池储能的充放电过程对循环老化的影响缺少考虑,这与电池单元本体的非线性等效电路模型相违背。因此,考虑电池储能的循环老化成本、需求侧响应,提出了交直流混合配电网动态重构方法。通过对实际电池储能的充放电功率的运行区域进行采样,在凸包络的采样点集合中将非理想状态下的电池储能模型重构为线性模型。在此基础上,分别建立了基于累积吞吐电量和放电深度的循环老化成本模型,构建了综合成本最小化的交直流混合配电网动态重构模型。并采用混合整数二阶锥规划方法求解。在改进的33节点算例上仿真验证了所提基于放电深度的循环老化成本模型的经济性,分析了交直流混合配电网重构方法经济性的影响因素。     

基于相量校正的多源配电网故障区段定位

为简化含分布电源的配电网故障定位算法过程,并提高故障定位算法的容错性,以矩阵算法为基础提出一种改进的区段定位算法。首先,建立描述节点区段关系的结构描述矩阵,并通过判断是否检测到过电流及过流方向建立故障信息矩阵。然后,利用结构描述矩阵和故障信息矩阵相乘得到故障判断矩阵,并根据区段对应矩阵中行元素构成的不同将区段划分类型,建立各类型区段下反映所有故障情况的故障判据。最后,对照故障判断矩阵行元素与故障判据,判断出发生故障的区段。针对配网中FTU上传信息易发生畸变的情况,提出了基于相量校正的信息畸变校正方法,通过故障电流与正常运行电流相位差异进行定位结果的修正,降低故障信息畸变对定位结果的影响。对DG的投切状况以及配电网两相和三相短路的单重多重故障进行仿真,结果验证了所提故障定位算法的原理简单、准确度好并具有一定的容错能力。     

基于定频MPC的飞跨电容储能双向直流变换器低频波动功率抑制策略

为抑制直流母线低频波动功率对储能系统的影响,以飞跨电容双向直流变换器为研究对象,提出基于单目标定频模型预测控制(model predictive control, MPC)低频波动功率抑制策略。为降低传统MPC算法的运算负荷,通过对变换器数学模型进行分析,提出一种单目标定频MPC控制算法。该算法仅需通过电感电流单目标约束函数,即可实现对系统电流及双端飞跨电容电压的控制,无须对两端飞跨电容电压进行独立寻优,极大地降低了计算量。为实现对储能系统低频波动功率的抑制,引入低频波动功率抑制算法,通过与所提MPC算法的融合,使最终控制方案能够在实现储能控制目标的前提下,具备低频波动功率抑制能力,且保持良好的动态性能。通过搭建小功率实验平台对所提控制策略的有效性进行了验证。     

基于多分区自适应改进果蝇算法的风光燃储微电网协调控制策略

为了使微电网控制系统中PI控制器的参数能够更好地适应可再生能源的随机性和波动性,提出了基于自适应步长的四分区多策略果蝇优化算法(fruit fly optimization algorithm, FOA)对PI参数进行实时优化。首先,以风光燃储微电网不同微源控制系统中的变换器为控制对象,建立微电网整体控制系统模型,基于此模型实时调整PI参数。然后,根据不同果蝇个体的适应度值将果蝇种群分为4个区,同时考虑4个区果蝇收敛性以及多样性的差异,设计不同的自适应更新策略。最后,采用所提算法对各微源控制过程中的PI参数进行寻优,与其他3种智能算法进行对比,验证了所提算法的可行性和优越性。仿真结果表明,所提算法可以使系统变换器响应速度更快,输出更加稳定。     

Amorphization Boost Multi-Ions Storage for High-Performance Aqueous Batteries

Regarding the complex properties of various cations, the design of aqueous batteries that can simultaneously store multi-ions with high capacity and satisfactory rate performance is a great challenge. Here an amorphization strategy to boost cation-ion storage capacities of anode materials is reported. In monovalent (H⁺, Li⁺, K⁺), divalent (Mg²⁺, Ca²⁺, Zn²⁺) and even trivalent (Al³⁺) aqueous electrolytes, the capacity of the resulting amorphous MoO_x is more than quadruple than that of crystalline MoO_x and exceeds those of other reported multiple-ion storage materials. Both experimental and theoretical calculations reveal the generation of ample active sites and isotropic ions in the amorphous phase, which accelerates cation migration within the electrode bulk. Amorphous MoO_x can be coupled with multi-ion storage cathodes to realize electrochemical energy storage devices with different carriers, promising high energy and power densities. The power density exceeded 15000 W kg⁻¹, demonstrating the great potential of amorphous MoO_x in advanced aqueous batteries.     

VA + Embeddings STAR: A State-of-the-Art Report on the Use of Embeddings in Visual Analytics

Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low-dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer-reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.     

Proofs of retrievability with tag outsourcing based on Goppa codes

In cloud storage, because the data owner loses the physical control of the data, the data may be tampered with or deleted. Although, it has been proposed to adopt provable data possession (PDP) or proofs of retrievability (POR) mechanism to ensure the integrity of cloud storage data. However, at present, most PDP/POR schemes are based on traditional cryptographic mechanisms and cannot resist quantum computer attacks. For this reason, the first POR scheme based on coding mechanism (BC-POR) is proposed in this paper. The scheme is constructed based on the difficulty assumptions of 2-regular word syndrome decoding (2-RWSD) problem and Goppa code distinguishing problem. Moreover, considering the low computing power of lightweight users, this paper adopts an audit scheme that supports the outsourcing of data tag calculation on the client side, that is, the calculation of data tag generation is outsourced to a third-party institution for execution. First of all, this scheme can prevent third-party institution from obtaining the real content of the data in the process of calculation tags and realize the privacy protection of user data. Secondly, the scheme uses a FSB hash function to generate a decodable syndrome, and this algorithm does not require iterative operations during the tagging process, thereby reducing the computational overhead of the tag. Finally, the provable security method is used to prove the security of the proposed scheme, and the performance of the proposed audit scheme is evaluated to prove the effectiveness of the scheme.     

MLATSO: A method for task scheduling optimization in multi-load AGVs-based systems

In the context of increasingly competitive intelligent manufacturing, the multi-load Automated guided vehicles (AGVs) based Automated Storage and Retrieval System (AS/RS) has been of particular interest, as reductions in the number of AGVs required can significantly decrease potential congestions and increase the system effectiveness. In comparison with the single-load AGVs system, more difficult and critical issue of scheduling multi-load AGVs to automate storage/retrieval missions and to maximize economic benefits remains unresolved. Therefore, we propose a task scheduling optimization method for multi-load AGVs-based systems, with which, the objectives of least number of occupied AGVs, shortest travel time and minimum conflicts can be met simultaneously. The experiments are conducted in various scenarios, and verify that our work can use fewer AGVs to optimize tasks delivery, which enables the AS/RS stakeholders to reach win-win results for system performance and AGVs investment, thus maximizing economic benefit.     

Improved Cloud Storage Encryption Using Block Cipher-Based DNA Anti-Codify Model

When it comes to data storage, cloud computing and cloud storage providers play a critical role. The cloud data can be accessed from any location with an internet connection. Additionally, the risk of losing privacy when data is stored in a cloud environment is also increased. A variety of security techniques are employed in the cloud to enhance security. In this paper, we aim at maintaining the privacy of stored data in cloud environment by implementing block-based modelling to boost the privacy level with Anti-Codify Technique (ACoT) and block cipher-based algorithms. Initially, the cipher text is generated using Deoxyribo Nucleic Acid (DNA) model. Block-cipher-based encryption is used by ACoT, but the original encrypted file and its extension are broken up into separate blocks. When the original file is broken up into two separate blocks, it raises the security level and makes it more difficult for outsiders to cloud data access. ACoT improves the security and privacy of cloud storage data. Finally, the fuzzy-based classification is used that stores various access types in servers. The simulation results shows that the ACoT-DNA method achieves higher entropy against various block size with reduced computational cost than existing methods.