Industrial Recycling Process of Batteries for EVs

The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Electrochemical storage devices,particularly lithium-ion batteries,are critical for this transition’s success.This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge.Given the environmental degradation caused by hazardous wastes and the scarcity of some resources,recycling used lithium-ion batteries has significant economic and practical importance.Many efforts have been undertaken in recent years to recover cathode materials(such as high-value metals like cobalt,nickel,and lithium).Regrettably,the regeneration of lower-value-added anode materials(mostly graphite)has received little attention.However,given the widespread use of carbon-based materials and the higher concentration of lithium in the anode than in the environment,anode recycling has gotten a lot of attention.As a result,this article provides the most recent research progress in the recovery of graphite anode materials from spent lithium ion batteries,analyzing the strengths and weaknesses of various recovery routes such as direct physical recovery,heat treatment recovery,hydrometallurgy recovery,heat treatment-hydrometallurgy recovery,extraction,and electrochemical methods from the perspectives of energy,environment,and economy;additionally,the reuse of recycled anode mats is discussed.Finally,the problems and future possibilities of anode recycling are discussed.To enable the green recycling of wasted lithium ion batteries,a low energy-consuming and ecologically friendly solution should be investigated.

Multi-dimensional Security Range Query for Industrial IoT

The Internet of Things(IoT)has allowed for significant advancements in applications not only in the home,business,and environment,but also in factory automation.Industrial Internet of Things(IIoT)brings all of the benefits of the IoT to industrial contexts,allowing for a wide range of applications ranging from remote sensing and actuation to decentralization and autonomy.The expansion of the IoT has been set by serious security threats and obstacles,and one of the most pressing security concerns is the secure exchange of IoT data and fine-grained access control.A privacypreserving multi-dimensional secure query technique for fog-enhanced IIoT was proposed in light of the fact that most existing range query schemes for fog-enhanced IoT cannot provide both multi-dimensional query and privacy protection.The query matrix was then decomposed using auxiliary vectors,and the auxiliary vectorwas then processed usingBGNhomomorphic encryption to create a query trapdoor.Finally,the query trapdoor may be matched to its sensor data using the homomorphic computation used by an IoT device terminal.With the application of particular auxiliary vectors,the spatial complexity might be efficiently decreased.The homomorphic encryption property might ensure the security of sensor data and safeguard the privacy of the user’s inquiry mode.The results of the experiments reveal that the computing and communication expenses are modest.

An Efficient Machine Learning Based Precoding Algorithm for Millimeter-Wave Massive MIMO

Millimeter wave communication works in the 30–300 GHz frequency range,and can obtain a very high bandwidth,which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation(5G).The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture.The resulting array gain can compensate for the path loss of the millimeter wave.Utilizing this feature,the millimeter wave massive multiple-input multiple-output(MIMO)system uses a large antenna array at the base station.It enables the transmission of multiple data streams,making the system have a higher data transmission rate.In the millimeter wave massive MIMO system,the precoding technology uses the state information of the channel to adjust the transmission strategy at the transmitting end,and the receiving end performs equalization,so that users can better obtain the antenna multiplexing gain and improve the system capacity.This paper proposes an efficient algorithm based on machine learning(ML)for effective system performance in mmwave massive MIMO systems.The main idea is to optimize the adaptive connection structure to maximize the received signal power of each user and correlate the RF chain and base station antenna.Simulation results show that,the proposed algorithm effectively improved the system performance in terms of spectral efficiency and complexity as compared with existing algorithms.

An Optimal Method for Supply Chain Logistics Management Based on Neural Network

From raw material storage through final product distribution,a cold supply chain is a technique in which all activities are managed by temperature.The expansion in the number of imported meat and other comparable commodities,as well as exported seafood has boosted the performance of cold chain logistics service providers.On the basis of the standard basicpursuit(BP)neural network,a rough BP particle swarm optimization(PSO)neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers.To reduce duplicate information in the original data and make the input index more compact,the model employs rough set.Instead of using gradient descent to train the weights of the neural network,particle swarm optimization is utilized to ensure that the output results are not readily caught in local minima and that the network’s generalization capacity is improved.Finally,an example is presented to demonstrate the model’s validity and viability.The findings reveal that the model’s prediction error is 40.94 percent lower than the BP neural network model,and the prediction result is more accurate and dependable,providing a new technique for cold chain food production companies to swiftly pick the best cold chain logistics service provider.

Modeling and Simulation of Two Axes Gimbal Using Fuzzy Control

The application of the guided missile seeker is to provide stability to the sensor’s line of sight toward a target by isolating it from the missile motion and vibration.The main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling approach.The paper is started by deriving the mathematical model for gimbals motion using Newton’s second law,followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/SimMechanics.Then,a Mamdani-type fuzzy and a Proportional-Integral-Derivative(PID)controllers were designed using MATLAB software.The performance of both controllers was evaluated and tested for different types of input shapes.The simulation results showed that self-tuning fuzzy controller provides better performance,since no overshoot,small steady-state error and small settling time compared to PID controller.