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.

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.

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.

There are more large-scale PV plants being established in rural areas due to availability of low priced land. However, distribution grids in such areas traditionally have feeders with low X/R ratios, which makes the independent reactive power compensation method less effective on voltage regulation. Consequently, upstream Step Voltage Regulator (SVR) may suffer from excessive tap operations with PV induced fast voltage fluctuations. Although a battery energy storage system (BESS) can successfully smooth PV generation, frequent charge/discharge will substantially affect its cost effectiveness. In this paper, a real-time method is designed to coordinate PV inverters and BESS for voltage regulation. To keep up with fast fluctuations of PV power, this method will be executed in each 5s control cycle. In addition, charging/discharging power of BESS is adaptively retuned by an active adjustment method in order to avoid BESS premature energy exhaustion in a long run. Finally, through a voltage margin control scheme, the upstream SVR and downstream PV inverters and BESS are coordinated for voltage regulation without any communication. This research is validated via an RTDS-MatLab co-simulation platform, and it will provide valuable insights and applicable strategies to both utilities and PV owners for large-scale PV farm integration into rural networks.

The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision.

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.