This research paper aims to design and implement an intelligent least short time memory (LSTM) deep learning classification technique to detect possible anomalies in measurements dataset within a particular Li-ion battery type. For the state of charge (SOC) and battery faults estimation, a Joint State and Parameter Extended Kalman Filter (JEKF) estimator is developed. The SOC accuracy performance is excellent, with less than 0.5% error during steady-state, compared to the 2% error reported in the literature. For the design and implementation of JEKF SOC and parameter estimation is chosen a preset Li-ion battery Simulink Simscape generic model. It is also helpful to generate the healthy and faulty measurement dataset to design and implement the proposed intelligent LSTM classifier deep learning technique. The generic Li-ion battery model is wisely selected for the “proof concept” purpose, model validation, and algorithms’ robustness, accuracy, and effectiveness. Compared to the traditional EKF fault diagnosis and isolation (FDI), a model-based estimation strategy, the proposed classification LSTM technique is an intelligent data-driven-based deep learning algorithm of high accuracy (around 80%) and loss performance close to zero. Therefore, this feature makes data collection of dataset measurements directly from Li-ion battery sensors possible, which is beneficial for generating online fault scenarios. Additionally, the LSTM deep learning technique can remarkably classify all detected anomalies with high accuracy, independent of battery model accuracy, uncertainties, and unmodeled dynamics. Also, high-performance accuracy root mean square error (RMSE) of 0.0588 (voltage fault), approximately $5.5\times {10}^{-7}$ (healthy) and 8.87 ×${10}^{-6}$ (current fault) for deep learning shallow neural network (DLSNN) reveals an obvious superiority of both compared to the traditional FDI estimation strategies.

Battery state of charge (SOC) accuracy plays a vital role in a hybrid electric vehicle (HEV), as it ensures battery safety in a harsh operating environment, prolongs life, lowers the cost of energy consumption, and improves driving mileage. Therefore, accurate SOC battery estimation is the central idea of the approach in this research, which is of great interest to readers and increases the value of its application. Moreover, an accurate SOC battery estimate relies on the accuracy of the battery model parameters and its capacity. Thus, the purpose of this paper is to design, implement and analyze the SOC estimation accuracy of two battery models, which capture the dynamics of a rechargeable SAFT Li-ion battery. The first is a resistor capacitor (RC) equivalent circuit model, and the second is a generic Simscape model. The model validation is based on the generation and evaluation of the SOC residual error. The SOC reference value required for the calculation of residual errors is the value estimated by an ADVISOR 3.2 simulator, one of the software tools most used in automotive applications. Both battery models are of real interest as a valuable support for SOC battery estimation by using three model based Kalman state estimators developed in Part 2. MATLAB simulations results prove the effectiveness of both models and reveal an excellent accuracy.

The Li-Ion battery state-of-charge estimation is an essential task in a continuous dynamic automotive industry for large-scale and successful marketing of hybrid electric vehicles. Also, the state-of-charge of any rechargeable battery, regardless of its chemistry, is an essential condition parameter for battery management systems of hybrid electric vehicles. In this study, we share from our accumulated experience in the control system applications field some preliminary results, especially in modeling, control and state estimation techniques. We investigate the design and effectiveness of two state-of-charge estimators, namely an extended Kalman filter and a proportional integral observer, implemented in a real-time MATLAB environment for a particular Li-Ion battery. Definitely, the aim of this work is to find the most suitable estimator in terms of estimation accuracy and robustness to changes in initial conditions (i.e., the initial guess value of battery state-of-charge) and changes in process and measurement noise levels. By a rigorous performance analysis of MATLAB simulation results, the potential estimator choice is revealed. The performance comparison can be done visually on similar graphs if the information gathered provides a good insight, otherwise, it can be done statistically based on the calculus of statistic errors, in terms of root mean square error, mean absolute error and mean square error.

Electrical contacts are important circuit components with diverse industrial applications, and their failure can lead to multiple unwanted effects. Hence, the behavior of electrical contacts is a widely studied topic in the scientific literature based on various approaches, tools, and techniques. The present study proposes a new approach to numerical modeling and simulation based on the Holm contact theory, aiming to study the dependence between the electric potential and the temperature within an electrical contact. Structured in five sections, the research was conducted using COMSOL Multiphysics software (version 5.3) and its solid-state mechanics, electric current, and heat transfer modules in order to highlight contact behavior from mechanical, electrical and thermal points of view: the von Mises stress, contact force, electric field amplitude, variation of the electrical potential along the current path, temperature gradient, and dependence of temperature along the contact elements edges were obtained by simulation, and are graphically represented. The results show that the temperature increase follows a parabolic curve, and that for values higher than 4 mV of voltage drop, the temperature of the contact increases to 79.25 degrees (and up to 123.81 degrees for 5 mV) over the ambient temperature, thus the integrity of insulation can be compromised. These values are close (10–12%) to the analytically calculated ones, and also in line with research assessed in the literature review.

Breakdown of stackers and excavators in opencast mines is possible because of operating, manufacturing and structural causes, and it produces high financial losses. These can be prevented by using various measures, including analyses and strength tests, with computerized modeling and simulation using FEA or other techniques being implemented in the recent years. In this paper a fatigue study is conducted on the boom of a BWE. Based on a computer model of the boom previously developed in SOLIDWORKS by our author team, first the modal analysis is conducted for three positions of the boom by studying the frequency response during the excavation process. This is followed by the time response determination corresponding to the maximum displacement frequency, in order to assess the stress during the excavation process, which causes the material fatigue in the boom structure. It was found that the maximum displacements appear when the BWE boom operates in a horizontal position. The aim was to estimate the period of time to failure in order to prevent unwanted accidents, and to develop a method that is applicable to any surface mining or industrial machine with similar structure.