Search

Electric vehicles have the advantages of zero emissions and high energy efficiency. They have a broad potential in today's social life, especially in China where they have been widely used. In the current situation, whereby the storage capacity of electric vehicles is continually increasing and the requirements for grid stability are getting higher and higher, V2G technology emerges to keep up with the times. Since the electric vehicle charging station is a large-scale electric vehicle cluster charging terminal, it is necessary to pay attention to the status and controllability of each charging pile. In view of the lack of attention to the actual operation of the electric vehicle charging station in the existing vehicle–network interaction mode, the charging state of the current electric vehicle charging station is fixed. In this paper, deep learning is used to establish a load perception model for electric vehicle charging stations, and K-means clustering is used to optimize the load perception model to realize random load perception and non-intrusive load monitoring stations for electric vehicle charging. The calculation example results show that the proposed method has good performance in the load perception and controllability evaluation of electric vehicle charging stations, and it provides a feasible solution for the practical realization of electric vehicle auxiliary response. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Earlier calculations of geomagnetically induced currents (GICs) in Chinese power grids have mainly concentrated on the highest voltage-level system whose geomagnetic risk is considered to be the largest, thus ignoring secondary voltage-level systems. With the 1000-kV system being newly added to China's power system, it is significant to figure out the interaction between GIC in the 500 kV [extremely high voltage (EHV)] system and GIC in the 1000 kV [ultra-high voltage (UHV)] system. Based on the North China-Central China-East China Power Grids, this paper establishes a single-voltage grid by only considering the 1000-kV system and a dual-voltage grid by considering the 1000- and 500-kV systems and investigates GIC in these two grids by developing their GIC “Full-node models.” The impact of the 500-kV system on GIC in the 1000-kV system is analyzed. GIC risks in the UHV and EHV systems are assessed by comparing calculated GIC data with monitored values of GIC. The results show that the impact of the 500-kV system on GIC in the 1000-kV system is obvious, both the EHV and the UHV grid have a high GIC risk. So calculating GIC in the UHV system and in the EHV system should utilize GIC modeling methods for multivoltage power grids. [ABSTRACT FROM PUBLISHER]

The voltage sag problem in the distribution network seriously affects the charging quality of electric vehicles. Therefore, evaluating the impact of voltage sag on electric vehicle charging is crucial for the development of electric vehicles. However, existing researches based on simulation models or independent power battery design and testing experiments cannot accurately and reliably reflect the charging interference of voltage sag on electric vehicles. This paper proposes for the first time the issue of voltage sag in China's distribution network affecting electric vehicle charging, and designs test cases to test the performance of electric vehicle charging under four kinds of sag parameters, two models of Brilliance BMW electric vehicles, two different temperatures, three brands of charging piles, and two vehicles charging simultaneously on the same power grid. In addition, this paper proposes a comprehensive solution for analyzing the impact of voltage sag on electric vehicle charging. Finally, an evaluation method based on the attention mechanism and BIGRU network has been developed to evaluate the adverse effect of voltage sag on the charging current of electric vehicles, with an accuracy of about 96.01% and a operating time of 0.4s. The superiority of the proposed method was further verified through comparative experiments with five mainstream methods. • Proposes a solution for analyzing the impact of power quality on vehicle charging. • Develop a novel method to evaluate the effect of voltage sag on vehicle charging. • Design cases based on real scenarios and the characteristics of voltage sag in China. • Develop a RBA-Net framework based on Bi-GRU network and attention mechanism. • A preprocessing method suitable for electric vehicle charging data is proposed. [ABSTRACT FROM AUTHOR]

Current electric bus has become an important public transport for people in China, and its interior sound quality directly affects the driver's physical and mental health for a long working time and the comprehensive performance of the vehicle. Therefore, it is an inevitable and urgent trend to research electric bus interior sound quality. Aiming at the problem of subjective uncomfortable auditory sensation caused by different speeds, the noise signals near the driver's ears were collected under two constant speed conditions. On the basis of principle analysis of feed-forward adaptive control system, the primary and secondary path transfer functions were presented using Simulink module in MATLAB to construct three active noise control models for an electric bus based on least mean square (LMS), normalized LMS (NLMS) and filtered-x LMS (FxLMS). Numerical simulation results indicate that the established control models are convergent, and four acoustic parameters are effectively reduced after controls to achieve the effect of improving the sound quality. [ABSTRACT FROM AUTHOR]

With the development of the electric vehicle industry, electric vehicles have provided more choices for people. However, the performance of electric vehicles needs improvement, which makes most consumers take a wait-and-see attitude. Therefore, finding a method that can effectively improve the performance of electric vehicles is of great significance. To improve the current performance of electric vehicles, a convex optimization algorithm is proposed to optimize the motor model and power battery parameters of electric vehicles, improving the overall performance of electric vehicles. The performance of the proposed convex optimization algorithm, dual loop DP optimization algorithm, and nonlinear optimization algorithm is compared. The results show that the hydrogen consumption of electric vehicles optimized by the convex optimization algorithm is 95.364 g. This consumption is lower than 98.165 g of the DCDP optimization algorithm and 105.236 g of the nonlinear optimization algorithm before optimization. It is also significantly better than the 125.59 g of electric vehicles before optimization. The calculation time of the convex optimization algorithm optimization is 4.9 s, which is lower than the DCDP optimization algorithm and nonlinear optimization algorithm. The above results indicate that convex optimization algorithms have better optimization performance. After optimizing the power battery using a convex optimization algorithm, the overall performance of electric vehicles is higher. Therefore, this method can effectively improve the performance of current electric vehicle power batteries, make new energy vehicles develop rapidly, and improve the increasingly serious environmental pollution and energy crisis in China. [ABSTRACT FROM AUTHOR]

Development of renewable power generation systems (RPGSs) is continually increasing worldwide with Germany, China, Japan, Italy and the USA are the leaders contributing to the largest development in each region. Due to the increasing number of RPGSs connected to the utility grid, power quality, safe operation, and islanding protection of RPGSs are becoming important issues, among others. One of the most important elements is the current control technique that must meet the requirements for grid interconnection according to international standards and practices. The RPGS itself must be safe and highly efficient for reliable and secure grid interconnection operations. This paper presents a review of the state-of-the-art of the current control techniques for three-phase grid-interconnection of RPGSs. Several current control architectures, their operations including advantages and disadvantages were discussed. Performance comparison and evaluation of several controllers were also reviewed. [ABSTRACT FROM AUTHOR]

Bicycle-sharing plays an important role in solving the "last mile" problem of Chinese people and promoting the construction of an environment-friendly society. This study assesses the carbon emissions generated in the life cycle of bicycle-sharing, examines the substitution effect of bicycle-sharing, and quantifies the depletion rate of bicycle-sharing in combination with the current rate of electric vehicles in China. The depletion rate of shared bicycles in China is high, roughly 5–15% per year. In addition, we discuss scenarios where the annual growth rate of electric vehicles is 40%, 50%, and 60%. Results show that when electric vehicles' depletion rate is 5% and the annual growth rate of electric vehicles is 40%, the largest net emission reduction in 2025 is 1.96 million tons. Additionally, when electric vehicles' depletion rate is 5% and the annual growth rate of electric vehicles is 60%, the smallest net emission reduction totals 1.7 million tons. [ABSTRACT FROM AUTHOR]

The vibration and friction triggered by the long-term operation of stacker inevitably lead to the damage problem of stacker’s track. In order to ensure the stable operation of the stacker, a novel scheme is proposed to address an accurate damage localization problem of the stacker’s track in industrial environment based on ensemble empirical mode decomposition-empirical mode decomposition (EEMD-EMD) and density-based spatial clustering of applications with noise (DBSCAN). First, the original electric current signal with unbalanced distribution is processed by fixed-interval smoothing and cubic Hermite interpolation algorithms. Second, the EEMD-EMD method, which solves the mode-mixing problem, is adopted to decompose the preprocessed signal into well-defined intrinsic mode functions (IMFs). Third, based on Hilbert–Huang transform (HHT) of the IMFs, the preprocessed signal and the difference of average instantaneous amplitudes in two adjacent time points are considered as the 2-D feature vector input of the DBSCAN algorithm. As such, damage locations of the stacker’s track are detected by means of the outliers that are obtained by the DBSCAN algorithm. The efficiency of the proposed localization scheme and its superiority over the existing cooperative damage localization methods, namely, the box-plot method, the pulse coupling neural network (PCNN) and wavelet transform (WT) theory method, and the box-plot and WT method, are verified through experiments using the data provided by State Grid Measuring Center of China. [ABSTRACT FROM AUTHOR]

Depletion of fossil fuel deposits is the main current issue related to the world's power generation. Renewable energy sources integrated with energy efficiency represent an effective solution. The electrification of end-use coupled with renewable power generation integration is considered as an important tool to achieve these tasks. However, the current electric power system does not currently have the suitable features to allow this change. Therefore, in the future, it has to allow two-way direction power flows, communication, and automated controls to fully manage the system and customers. The resulting system is defined as the smart grid. This article analyses the smart grid state of play within China, the US, and the EU, assessing the completion state of each smart grid technology and integrated asset. The analysis related to these countries presented here shows that the smart grid overall state of play in China, the US, and the EU are equal to 18%, 15%, and 13%, respectively, unveiling the need related to further efforts and investments in these countries for the full smart grid development. [ABSTRACT FROM AUTHOR]