Performance of Cathodes Fabricated from Mixture of Active Materials Obtained from Recycled Lithium-Ion Batteries

The cathode performance of lithium-ion batteries (LIBs) fabricated from recycled cathode active materials is studied for three scenarios. These scenarios are based on the conditions for separation of different cathode active materials in recycling facilities during the LIB’s recycling process. In scenario one, the separation process is performed ideally, and the obtained pure single cathode active material is used to make new LIBs after regeneration. In scenario two, the separation of active materials is performed with efficiencies of less than 100%, which is the actual case in the recycling process. In this scenario, a single cathode active material that contains a little of the other types of cathode active materials is used to make new LIBs after the materials’ regeneration. In scenario three, the separation has not been performed during the recycling process. In this scenario, all types of cathode active materials are regenerated together, and a mixture is used to make new LIBs. The studies are performed through modeling and computer simulation, and several experiments are conducted for validation purposes. The cathode active materials that are studied are the five commercially available cathodes made of LiMn2O4 (LMO), LiCoO2 (LCO), LiNixMnyCo(1−x−y)O2 (NMC), LiNixCoyAl(1−x−y)O2 (NCA), and LiFePO4 (LFP). The results indicate that the fabrication of new LIBs with a mixture of cathode active materials is possible when cathode active materials are not ideally separated from each other. However, it is recommended that the separation process is added to the recycling process, at least for the separation of LFP or reducing its amount in the cathode active materials mixture. This is because of the difference of the voltage level of LFP compared to the other studied active materials for cathodes.

Voltage-Based Load Recognition in Low Voltage Distribution Grids with Deep Learning

Due to the increasing penetration of renewable energies in lower voltage level, there is a need to develop new control strategies to stabilize the grid voltage. For this, an approach using deep learning to recognize electric loads in voltage profiles is presented. This is based on the idea to classify loads in the local grid environment of an inverter’s grid connection point to provide information for adaptive control strategies. The proposed concept uses power profiles to systematically generate training data. During hyper-parameter optimizations, multi-layer perceptron (MLP) and convolutional neural networks (CNN) are trained, validated, and evaluated to determine the best task configurations. The approach is demonstrated on the example recognition of two electric vehicles. Finally, the influence of the distance in a test grid from the transformer and the active load to the measurement point, respectively, onto the recognition accuracy is investigated. A larger distance between the inverter and the transformer improved the recognition, while a larger distance between the inverter and active loads decreased the accuracy. The developed concept shows promising results in the simulation environment for adaptive voltage control.

A Battery Voltage Level Monitoring System for Telecommunication Towers

Voltage fluctuations in batteries form a major challenge the telecommunication towers face. These fluctuations mostly occur due to poor management and the lack of a battery voltage level monitoring system. The current paper presents a battery voltage-level monitoring system to be used in telecommunication towers. The proposed solution is incorporated with a centralized mobile application dashboard for accessing the live data of the installed battery, integrated with voltage-level, current, temperature, fire, and gas sensors. An Arduino Uno microcontroller board is used to process and analyze the collected data from the sensors. The Global Service Message (GSM) module is used to monitor and store data to the cloud. Users are alerted in the case of low voltage, fire, and increase in harmful gases in the tower through Short Message Service (SMS). The experiment was conducted at Ngorongoro and Manyara telecommunication towers. The developed system can be used in accessing battery information remotely while allowing real-time continuous monitoring of battery usage. The proposed battery voltage-level monitoring system contributes to the elimination of battery hazards in towers. Therefore, the proposed battery voltage level monitoring system can be adopted by telecommunication tower engineers for the reduction of voltage fluctuation risks.

Research on UHV Isolating Switch Technology with Multi-source Action Signal

With the improvement of the voltage level of power transmission and transformation lines, the development of smart grids has highlighted a major demand, and relevant enterprises and scientific researchers in the UHV field at home and abroad have explored the research of water intelligent isolation switch equipment. This article explains the current problems of traditional substation isolation switches, and analyzes the advantages and disadvantages of UHV isolation switch technology and its application status through three aspects: isolation switch, isolation switch contact and isolation switch sensor. Studies have shown that the development and application of isolating switches with multi-source action signals, and further improving the proportion and accuracy of smart devices to replace manual labor are the development trends of UHV isolating switches.