This article describes a multi-point optical fiber-based sensor for the measurement of electrolyte density in lead-acid batteries. It is known that the battery charging process creates stratification, due to the different densities of sulphuric acid and water. In order to study this process, density measurements should be obtained at different depths. The sensor we describe in this paper, unlike traditional sensors, consists of several measurement points, allowing density measurements at different depths inside the battery. The obtained set of measurements helps in determining the charge (SoC) and state of health (SoH) of the battery.

In battery applications, particularly in automobiles, submarines and remote communications, the state of charge (SoC) is needed in order to manage batteries efficiently. The most widely used physical parameter for this is electrolyte density. However, there is greater dependency between electrolyte viscosity and SoC than that seen for density and SoC. This paper presents a Quartz Crystal Microbalance (QCM) sensor for electrolyte density-viscosity product measurements in lead acid batteries. The sensor is calibrated in H₂SO₄ solutions in the battery electrolyte range to obtain sensitivity, noise and resolution. Also, real-time tests of charge and discharge are conducted placing the quartz crystal inside the battery. At the same time, the present theoretical “resolution limit” to measure the square root of the density-viscosity product ( ) of a liquid medium or best resolution achievable with a QCM oscillator is determined. Findings show that the resolution limit only depends on the characteristics of the liquid to be studied and not on frequency. The QCM resolution limit for measurements worsens when the density-viscosity product of the liquid is increased, but it cannot be improved by elevating the work frequency.

This research work is aimed at identifying the applicability of additive manufacturing (AM) to spare parts in the automotive manufacturing industry, specifically in the field of preventive maintenance. A procedure of AM applicability to preventive maintenance is presented. To this end, the results of the application of a case that has been carried out in the Stellantis factory located in the Vigo (Spain) area have been obtained. The procedure was based on an initial inventory from which a series of lists have been obtained that were later categorised. This was performed in order to know the technical and economic applicability of polymeric spare parts replacement by parts made of AM in polyamide material (PA12) through two manufacturing methods (HP Multi-Jet and Selective Laser Sintering technology, SLS). Original spare parts were characterised and compared with replacement parts made in AM. This article provides observations, recommendations and conclusions to summarise the situation regarding the use of AM techniques to improve preventive maintenance.

The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R²). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1–1 mm, 3.298 kN and 4.1 mm for the case of 0.5–0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5–1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1–1 mm case was the welding current; for the 0.5–0.5 mm case, it was pulse welding; and for the 0.5–1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg–Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures.

This paper presents the development of a low-power electronic system powered by a thermoelectric generator, based on Peltier cells, which takes advantage of daily temperature variations in the environment using a tank with 6 liters of water as a thermal accumulator. The generator works both day and night, since it uses a dc-dc boost converter with bipolar input. The system avoids using batteries because it charges a 0.1 F supercapacitor module between 2.40 and 3.15 V, so the useful energy in each charging cycle is 208 mJ. This energy is sufficient so that the system can measure the ambient temperature, represent it on a display and record it along with the date and time on a memory card when there is enough stored energy. The PSpice model and the simulation of the generator is also presented and experimental results of its operation are presented for two days. It is derived that in that period, with a maximum temperature difference of 10 °C, the system is able to charge the supercapacitor thirteen times per day, which means a daily useful energy of 2.7 J. Finally, results of the autonomous system performance for four days are presented.

This work presents a software tool in order to facilitate the learning of the dc choppers. This tool consists of an integrated web-access set of multimedia animation-clips which present, in an interactive way, how the different dc choppers topologies work. The different topologies are classified depending on their quadrant operation and then the user can realize the circuit's performance from the simplest class-A to the four quadrant operation class-E converter. To do this, all circuit parameters including load values, duty ratio, and frequency may be selected by the user. Moreover, the load includes an electromotive force (EMF) that allows understanding the energy flow between the source and the load and then, the quadrant operation. Other useful characteristic of the tool is the way in which the circuits are drawn because they can be shown as a bridge or in their simplest form in the case of only the essentials devices being shown. The information about the currents and voltages paths is dynamically presented with sliding rules over the graphs during simulations indicating the state of semiconductor switches by means of changing colours. The multimedia system includes documentation on all the subjects, showing basic concepts and equations, in pdf format and links to industrial manufacturers.