Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.

Due to the importance of induction motors in a wide variety of industrial processes, it is crucial to properly identify abnormal conditions in order to avoid unexpected stops. The inter-turn short circuit (ITSC) is a very common failure produced with electrical stresses and affects induction motors (IMs), leading to catastrophic damage. Therefore, this work proposes the use of the empirical wavelet transform to characterize the time frequency behavior of the IM combined with a self-organizing map (SOM) structure to perform an automatic detection and classification of different severities of ITSC. Since the amount of information obtained from the empirical wavelet transform is big, a genetic algorithm is implemented to select the modes that allow a reduction in the quantization error in the SOM. The proposed methodology is applied to a real IM during the start-up transient considering four different fundamental frequencies. The results prove that this technique is able to detect and classify three different fault severities regardless of the operation frequency.

New technological and scientific advances in the development of sensors and actuators demand the development of new devices to deal with recent problems and challenges in these new and emerging processes. Moreover, paper-based devices have tremendous potential for developing actuators as paper exhibits capillary transport and hygroexpansion due to swelling of the fibers when absorbing water. Therefore, this paper proposes a mini actuator that is based on a hygro-thermal-paper-based cantilever beam that is activated by means of a droplet of an aqueous solution in combination with a circulating electrical current to analyze its response. The contribution of this proposal includes the analysis of the flexural response of the mini actuator when it is tested by using two different solutions: distilled water and a water/alcohol solution. Additionally, four cases related to the droplet volume are studied and a statistical analysis of the bending responses is presented. The results achieved show that that water-alcohol solutions have a lower deviation in comparison with water only. Moreover, it is demonstrated that a specific change in the maximum displacement is obtained according to the volume and the type of solution. Thus, it is suggested that the response of the mini actuator can be tuned using different aqueous solutions.