Lithium-ion batteries stand out from other clean energy sources because of their high energy density and small size. With the increasing application scope and scale of lithium-ion batteries, real-time and accurate monitoring of its state of health plays an important role in ensuring the healthy and stable operation of an energy storage system. Due to the interaction of various aging reactions in the aging process of lithium-ion batteries, the capacity attenuation shows no regularity. However, the traditional monitoring method is mainly based on voltage and current, which cannot reflect the internal mechanism, so the accuracy is greatly reduced. Recently, with the development of electrochemical impedance spectroscopy, it has been possible to estimate the state of health quickly and accurately online. Electrochemical impedance spectroscopy can measure battery impedance in a wide frequency range, so it can reflect the internal aging state of lithium-ion batteries. In this paper, the latest impedance spectroscopy measurement technology and electrochemical impedance spectroscopy based on lithium-ion battery health state estimation technology are summarized, along with the advantages and disadvantages of the summary and prospects. This fills the gap in this aspect and is conducive to the further development of this technology.

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Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area.

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A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms

Abstract Lithium-ion batteries (LIBs) are crucial for the large-scale utilization of clean energy. However, because of the complexity and real-time nature of internal reactions, the mechanism of capacity decline in LIBs is still unclear. This has become a bottleneck restricting their promotion and application. Electrochemical impedance spectroscopy (EIS) contains rich electrochemical connotations and significant application prospects, and has attracted widespread attention and research on efficient energy storage systems. Compared to traditional voltage and current data, the state-of-health (SOH) estimation model based on EIS has higher accuracy. This paper categorizes EIS measurement methods based on different principles, introduces the relationship between LIBs aging mechanism and SOH, and compares the advantages of different SOH estimation methods. After a detailed analysis of the latest technologies, a review is given. The insights of this review can deepen the understanding of the relationship between EIS and the aging effect mechanism of LIBs, and promote the development of new energy storage devices and evaluation methods. 

State-of-health estimation of lithium-ion batteries based on electrochemical impedance spectroscopy: a review

Abstract New energy storage devices such as batteries and supercapacitors are widely used in various fields 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 affect the performance of energy storage devices. Therefore, to maximize the efficiency 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 different innovative points are listed, their advantages and disadvantages are summarized, and the application of optical fiber sensors is emphasized. Finally, the challenges and prospects for these studies are described. The intent is to encourage researchers in relevant fields to study the early warning of safety accidents from the root causes. 

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

With the continuous developments of information technology, advanced computer technology and information technology have been promoted and used in the field of music. As one of the products of the rapid development of information technology, Artificial Intelligence (AI) involves many interdisciplinary subjects, adding new elements to music education. By analyzing the advantages of AI in music education, this paper systematically summarizes the application of AI in music education and discusses the development prospects of AI in music education. With the aid of AI, the combination of intelligent technology and on-site teaching solves the lack of individuation in the traditional mode and enhances students’ interest in learning.

/pdf/developments-and-applications-of-artificial-intelligence-in-3nzq6h58.pdf

Developments and Applications of Artificial Intelligence in Music Education

Human–machine interaction (HMI) allows the transfer of human intent to the robot and the collection of feedback from the robot. Human perception of the outside world is a direct response to human brain training, but low-level perception movements of robots are more complex than advanced reason. In the study, we designed a self-powered electronic skin (SPES) based on a piezoelectric nanogenerator (PENG). SPES shows a good piezoelectric effect and certain mechanical flexibility and can realize gesture recognition and tracking by sensing external pressure stimulation in HMI. Through the feedback control of the closed-loop system, the equivalent behavior trigger of the real intelligent robotic capacitive touch sensors (CATS) and SPES can be realized. SPES and Robot Hall Rotary Position Sensor (HRPS) have small perceptual errors, and remote signal feedback and behavior control of intelligent robots are realized by wireless transmission equipment, which is a greater advantage in a flexible environment. The proposed SPES tracking application in the HMI has great potential in the future artificial intelligence environment, the auxiliary equipment for people with disabilities, and the human–machine cooperation in a flexible environment. 

Self-Powered Electronic Skin for Remote Human–Machine Synchronization

The design of asphalt pavements in china is currently based on the multilayered elastic method, which is analytical in nature and yields stresses, strains, and deflections in the pavement system for a particular loading condition and pavement geometry, which are compared with established failure criteria to determine the performance of the given pavement. This design approaches is deterministic. In this paper, typical asphalt pavement structure reliability analysis was performed in which factors that affect pavement reliability regarded as input random , pavement surface deflection, layers of bottom stress and limit state function regarded as output variables , by reliability tool infinite element analysis, base on Monte Carlo’s Latin hypercube sampling method.At last the paper pertinently offered decision basis for improve the reliability of pavement structure and important reference values for drafting and selecting of asphalt pavement design parameters through calculating the reliability of pavement structure, sensitivity analysis of the design parameters is made.

Reliability Analysis of Asphalt Pavement Structure Based on Three-Dimensional Finite Element

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Papers

Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries

A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms

Self-Powered Electronic Skin for Remote Human–Machine Synchronization

Reliability Analysis of Asphalt Pavement Structure Based on Three-Dimensional Finite Element