In this paper, the approximation of a fractional-order PIDcontroller is proposed to control a DC–DC converter. The synthesis and tuning process of the non-integer PID controller is described step by step. A biquadratic approximation is used to produce a flat phase response in a band-limited frequency spectrum. The proposed method takes into consideration both robustness and desired closed-loop characteristics, keeping the tuning process simple. The transfer function of the fractional-order PID controller and its time domain representation are described and analyzed. The step response of the fractional-order PID approximation shows a faster and stable regulation capacity. The comparison between typical PID controllers and the non-integer PID controller is provided to quantify the regulation speed introduced by the fractional-order PID approximation. Numerical simulations are provided to corroborate the effectiveness of the non-integer PID controller.

Today, electric vehicles available in the market aspire to offer different connections to the end user, for instance, Vehicle to Grid (V2G), Vehicle to Building (V2B), Vehicle to Home (V2H), Vehicle to Vehicle (V2V), and Vehicle to Load (V2L), among others. Notwithstanding these versatility options toward the development of a sustainable society, the additional degradation of the energy storage systems once those operate in extra discharge modes is inevitable. Therefore, in this paper, an energy management strategy (EMS) which operates autonomously and noninvasively as an additional layer to the battery management system (BMS) is proposed. The EMS limits the current flow avoiding high and low temperatures, low state of charge (SoC), high deep of discharge (DoD), noncentered DoD around an optimal SoC point, and high charge and discharge rates. The proposed EMS is evaluated by long‐term simulations with a Li‐Ion battery degradation model and realistic weather conditions, during standard driving cycles including the V2L operation. The effectiveness and simplicity of tuning of the proposed EMS allow estimating and increasing the life expectancy of the Li‐Ion battery bank, by limiting the energy used for V2L operation.

The design and implementation of an energy-management system (EMS) applied to a residential direct current microgrid (DC-µG) is presented in this work. The proposed residential DC-µG is designed to provide a maximum power of one kilowatt by using two photovoltaic arrays (PAs) of 500 W, a battery bank (BB) of 120 V–115 Ah, a supercapacitor module of 0.230 F and a bidirectional DC–AC converter linked to the AC main grid (MG). The EMS works as a centralized manager and it defines the working operation mode for each section of the DC-µG. The operation modes are based on: (1) the DC-link bus voltage, (2) the generated or demanded power to each section of the DC-µG and (3) the BB’s state of charge. The proposed EMS—during the several working operation modes and at the same time—can obtain the maximum energy from the PAs, reduce the energy consumption from the main grid and keep the DC-link bus voltage inside a range of 190 V ± 5%. The EMS and local controllers are implemented by using LabVIEW and NI myRIO-1900 platforms. Moreover, experimental results during connection and disconnection of each DC-µG sections and different on-the-fly transitions are reported, these results focus on the behavior of the DC bus, which shows the DC bus robustness and stability. The robustness of the DC-µG is demonstrated by maintaining a balance of energy between the sources and loads connected to the DC bus under different scenarios.

ResumenEl diseño de experimentos desempeña un papel importante dentro del campo de diseño y optimización de procesos y productos, particularmente en áreas de manufactura y mejora continua. Dentro del diseño de experimentos existen múltiples áreas, una de ellas es el diseño robusto. El diseño paramé-trico robusto es un principio que enfatiza la correcta selección de niveles de factores de “control”, que hacen al producto robusto a la variabilidad introducida por otro grupo de factores llamados factores de “ruido”. Este artículo se enfoca en la realización de un estudio comparativo entre dos metodologías conocidas de diseño robusto: el enfoque tradicional y el enfoque de Taguchi, haciendo especial hincapié en el efecto que tienen las interacciones de control por control sobre las condiciones óptimas de operación. Los resultados mostraron que los arreglos cruzados de Taguchi no tienen la capacidad para estimar todos los términos significativos en un modelo. El resultado de las optimizaciones concluye que el enfoque de Taguchi es menos eficiente que el enfoque tradicional, tanto al minimizar como al maximizar.AbstractDesign of experiments plays an important role in the field of creating and innovating process and products directly in manufacturing and improving areas. There are several areas into designs of experiments; robust design is one of them. Robust parameter design is a principle that emphasize in products creation through a correct selection of values called “control” which make a product robust to the variability by the noise introducing by another factors known as “noise” factors. This article aims for a comparative study between two well-known robust design methodologies, making a special emphasis in the control by control interaction effects over optimal operating conditions. The results showed that Taguchi´s crossed arrays are unable to estimate all significant terms in a model. The optimizations result concludes that the Taguchi´s approach is less efficient than the traditional approach in both; maximization and minimization

The incorporation of high precision vehicle positioning systems has been demanded by the autonomous electric vehicle (AEV) industry. For this reason, research on visual odometry (VO) and Artificial Intelligence (AI) to reduce positioning errors automatically has become essential in this field. In this work, a new method to reduce the error in the absolute location of AEV using fuzzy logic (FL) is presented. The cooperative data fusion of GPS, odometer, and stereo camera signals is then performed to improve the estimation of AEV localization. Although the most important challenge of this work focuses on the reduction in the odometry error in the vehicle, the defiance of synchrony and the information fusion of sources of different nature is solved. This research is integrated by three phases: data acquisition, data fusion, and statistical evaluation. The first one is data acquisition by using an odometer, a GPS, and a ZED camera in AVE’s trajectories. The second one is the data analysis and fuzzy fusion design using the MatLab 2019^® fuzzy logic toolbox. The last is the statistical evaluation of the positioning error of the different sensors. According to the obtained results, the proposed model with the lowest error is that which uses all sensors as input (stereo camera, odometer, and GPS). It can be highlighted that the best proposed model manages to reduce the positioning mean absolute error (MAE) up to 25% with respect to the state of the art.

Multivariate Statistical Process Control (MSPC) seeks to monitor several quality characteristics simultaneously. However, it has limitations derived from its inability to identify the source of special variation in the process. In this research, a proposed model that does not have this limitation is presented. In this paper, data from two scenarios were used: (A) data created by simulation and (B) random variable data obtained from the analysed product, which in this case corresponds to cheese production slicing process in the dairy industry. The model includes a dimensional reduction procedure based on the centrality and data dispersion. The goal is to recognise a multivariate pattern from the conjunction of univariate variables with variation patterns so that the model indicates the univariate patterns from the multivariate pattern. The model consists of two stages. The first stage is concerned with the identification process and uses Moving Windows (MWs) for data segmentation and pattern analysis. The second stage uses Bayesian Inference techniques such as conditional probabilities and Bayesian Networks. By using these techniques, the univariate variable that contributed to the pattern found in the multivariate variable is obtained. Furthermore, the model evaluates the probability of the patterns of the individual variables generating a specific pattern in the multivariate variable. This probability is interpreted as a signal of the performance of the process that allows to identify in the process a multivariate out-of-control state and the univariate variable that causes the failure. The efficiency results of the proposed model compared favourably with respect to the results obtained using the Hotelling’s $T^2$ chart, which validates our model.