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Autonomous electric vehicles (AEVs) have garnered increasing attention in recent years as they hold significant promise for transforming the transportation sector. However, despite advances in the field, effective vehicle drive control remains a critical challenge that must be addressed to realize the full potential of AEVs. This study presents a novel approach to AEV drive control for concurrently generating a suitable speed profile and controlling the vehicle drive speed along a planned path that takes into account various driving circumstances that mimic real-world driving. The designed strategy is divided into two parts: The first part presents a proposed speed planning algorithm (SPA) that works on developing an adequate speed profile for vehicle navigation; first, the algorithm uses an approach for identifying sharp curves on the predefined trajectory; secondly, based on the dynamic properties of these curves, it generates an appropriate speed profile to ensure smooth vehicle travel across the entire trajectory with varying velocities. The second part proposes a new back-stepping control technique with a space vector modulation (SVM) strategy to control the speed of an induction motor (IM) as a traction part of the AEV. A load torque observer has been designed to enhance the speed-tracking task, while the system stability has been proven using Lyapunov theory. Through a series of experiments and simulations using MATLAB/Simulink software and the dSPACE 1104 real-time interface, we demonstrate the effectiveness of the SPA combined with the back-stepping control technique and highlight its potential to advance the field of AEV technology. Our findings have important implications for the design and implementation of AEVs and provide a foundation for future research in this exciting area of study.

Lithium-sulfur (Li-S) batteries can provide at least three times higher energy density than lithium-ion (Li-Ion) batteries. However, Li-S batteries suffer from a phenomenon called the polysulfide shuttle (PSS) that prevents the commercialization of these batteries. The PSS has several undesirable effects, such as depletion of active materials from the cathode, deleterious reactions between the lithium anode and electrolyte soluble lithium polysulfides, resulting in unfavorable coulombic efficiency, and poor cycle life of the battery. In this study, a new sulfur cathode composed of graphitic nitride as the polysulfide absorbing material and reduced graphene oxide as the conductive carbon host has been synthesized to rectify the problems associated with the PSS effect. This composite cathode design effectively retains lithium polysulfide intermediates within the cathode structure. The S@RGO/GN cathode displayed excellent capacity retention compared to similar RGO-based sulfur cathodes published by other groups by delivering an initial specific capacity of 1415 mA h g−1 at 0.2 C. In addition, the long-term cycling stability was outstanding (capacity decay at the rate of only 0.2% per cycle after 150 cycles).

The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches to perform early bearing damage identification in induction motors. This approach is unfeasible in most industrial applications because these machines are unable to perform their nominal functions under damaged conditions. In addition, many machines are installed at inaccessible sites or their housing prevents the setting of new sensors. Otherwise, current signature monitoring is available in most industrial machines because the devices that control, supply and protect these systems use the stator current. Another significant advantage is that the stator phases lose symmetry in bearing damaged conditions and, therefore, are multiple independent sources. Thus, this paper introduces a new approach based on fractional wavelet denoising and a deep learning algorithm to perform a bearing damage diagnosis from stator currents. Several convolutional neural networks extract features from multiple sources to perform supervised learning. An information fusion (IF) algorithm then creates a new feature set and performs the classification. Furthermore, this paper introduces a new method to achieve positive unlabeled learning. The flattened layer of several feature maps inputs the fuzzy c-means algorithm to perform a novelty detection instead of clusterization in a dynamic IF context. Experimental and on-site tests are reported with promising results.

Forecasting has extreme importance in industry due to the numerous competitive advantages that it provides, allowing to foresee what might happen and adjust management decisions accordingly. Industries increasingly use sensors, which allow for large-scale data collection. Big datasets enable training, testing and application of complex predictive algorithms based on machine learning models. The present paper focuses on predicting values from sensors installed on a pulp paper press, using data collected over three years. The variables analyzed are electric current, pressure, temperature, torque, oil level and velocity. The results of XGBoost and artificial neural networks, with different feature vectors, are compared. They show that it is possible to predict sensor data in the long term and thus predict the asset’s behaviour several days in advance.