Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. Principal component analysis (PCA) obtained better results than other algorithms. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. The results showed that fuel cell performance does not only depend on the supply of the reactants. A single neuro-proportional–integral–derivative (neuro-PID) controller is not able to stabilize the output voltage without the support of an inverse model control that includes the impact of the other variables on the fuel cell performance. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures.

Currently there is an increase in the demand for electric vehicles that require greater autonomy for driving. The use of suitable devices for energy recovery is crucial to guarantee the autonomy of electric vehicles (EV). In this paper, regenerative braking is used as an energy recovery strategy in an electric vehicle that uses a BLDC motor and a three-phase inverter for control it. The comparison of the inverter was made using SIHP22N60AE-BE3 MOSFET's and the IRAMY20UP60B module that uses IGBT's (with similar characteristics) The experimental results confirm that the module IRAMY recovers slightly more energy than MOSFET's. Therefore, it is determined that using the IRAMY module improves the amount of energy recovered after a braking situation for a braking cycle of 1s, with a duty cycle of 40%, a braking torque of 30Nm and 78RPM, and, although, the amount of energy recovered is not significant between these devices, for braking situations with higher torque, and in longer periods of braking, the autonomy of the EV is improved. For this work, the tests performed on the EV were done with no mechanical load.

There is currently an increasing demand for electric vehicles that require greater autonomy and energy efficiency when driving them. Control strategies in energy recovery systems are crucial to optimize the amount of energy returned to the battery and to ensure safety and stability for the user. In this paper, active fault tolerant control systems (AFTC) and passive fault tolerant control systems (PFTC) with other specialized control strategies (Fuzzy Logic, Neural Networks and Perturbation Rejection Controllers) are compared with classical PID controllers.The results of the simulations show that, keeping the battery voltage constant, returns of about 12% of the battery charge capacity are achieved while the braking time of the vehicles is reduced.

This paper presents a novel biomechatronic device that resolves the necessities of mobility for people with spinal cord trauma (SCI) and disability. The proposed device features a safe and reliable mobility mechanism that withstands daily use without premature mechanical wear, facilitating the activities of daily living (ADL) for people affected by SCI, integrating them to a social and workforce environment that allows them, on one hand, to move in a standing upright position in complex situations of the urban architecture, and on the other hand, provides them a mechatronic system to assist them to stand up and sit down.