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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.

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.

In recent years paper-based microfluidic systems have emerged as versatile tools for developing sensors in different areas. In this work; we report a novel physical sensing principle for the characterization of liquids using a paper-based hygro-mechanical system (PB-HMS). The PB-HMS is formed by the interaction of liquid droplets and paper-based mini-structures such as cantilever beams. The proposed principle takes advantage of the hygroscopic properties of paper to produce hygro-mechanical motion. The dynamic response of the PB-HMS reveals information about the tested liquid that can be applied to characterize certain properties of liquids. A suggested method to characterize liquids by means of the proposed principle is introduced. The experimental results show the feasibility of such a method. It is expected that the proposed principle may be applied to sense properties of liquids in different applications where both disposability and portability are of extreme importance.

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