Energy-efficient storage systems are crucial in the rapidly evolving electric vehicle (EV) technology landscape. This study presents an innovative strategy for real-time charging of deep-cycle batteries by integrating solar photovoltaic (PV) panels with power from the grid. The model relies on the effects of the Urban Dynamometer Driving Schedule (UDDS) and aims to determine the optimal deep-cycle battery capacity sizing. The initial phase involves designing the dimensions of the valence batteries, each including four parallel cells with a capacity of 59Ah. The adequate capacity of the battery pack is determined by evaluating the energy demands, considering the average duration of daylight, existing weather conditions (derived from Bahia Blanca, Argentina), and accessibility of the power grid as a supplementary resource. The proposed model integrates solar panels connected to a battery pack by using a double closed-loop control converter. This study emphasises the significance of an empirical analysis of the UDDS load profile to identify daily energy consumption trends and Depth of Discharge (DoD). The model evaluates the system’s overall efficiency, incorporating conversion losses using the Maximum Power Point Tracking (MPPT) algorithm, which maximises solar charging during peak sunlight hours while augmenting with grid power as needed. The most suitable machine learning (ML) algorithm for the observed DoD is predicted and cross-validated using nine selected machine learning approaches and evaluation metrics: Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). ML algorithms examine battery data in conjunction with a comparative analysis of performance indicators. The model simulation evaluated the possibility of solar energy capture, considering the capacity of the converter and its influence on efficient charging capabilities. The employed methodology examines solar energy utilisation alongside grid power augmentation and promotes a sustainable and efficient electric vehicle charging solution.

In this article, unlike the available literature, an optimal theorem is presented to design conditioning circuits for thermoelectric generator (TEG) energy harvesting without previous ad‐hoc assumptions about the electric/electronic structure. The results show that simple linear time‐invariant (LTI) conditioning circuits plus an extra non‐linear element transforming the TEG's constant voltage into a square wave (ring oscillator) are in fact the optimal choice to pick up the biggest output's energy. This research opens the door to new developments: ring oscillators with ultra‐low voltages (less than 1 mV) and non‐linear optimal control techniques applied to micro energy harvesting. Real measurement along with LT‐Spice simulations is presented to validate the theoretical results, presenting transfer functions for simulation and modeling purposes.

The purpose of this paper is to present a method for analyzing the stability of DC-DC boost converters providing a universal transfer function. This method relies on the application of the Laplace transform without any ad-hoc linear approximation or system linearization. A common methodology for evaluating the stability of DC-DC converters is the average method, which is essentially a linearization. In this paper, the Laplace transform is applied directly, resulting in a universal Z-transform model being used to design controllers and stability analyses as a discrete unifying transfer function. The examples cover both power and low voltage converters. Matlab simulations have been conducted to verify the theoretical findings. In particular, the second example considers a small 5V to 12V boost converter, which was previously examined in a Texas Instruments application note. The transfer functions and the Bode plot are provided.

In this study, the problem of finding an optimal controller for nonlinear systems with one input and a reference tracking signal is approached. With the problem's formulation, any desired signal can be tracked instantly with a closed-loop controller without the need for integral terms. Presentation lies at the heart of optimal control. This study, however, does not consider the integral term, allowing tracking and stability to occur naturally. It has a broad scope with a wide range of applications, namely when dealing with affine nonlinear systems, which provide geometric control unification with asymptotic stability in some cases. A common scenario that comes from optimal control involves the minimization of integral cost functionals. Issues like asymptotic stability or even tracking to the desired reference signal have always been the main limitations. In this study, the main theorem allows the solution of optimal control problems with no-integral terms, in other words tracking problems with input/state constraints, providing closed-loop controllers. A DC motor with a pendulum in upright position is an example of an application for which singular optimal control is tested in this study. The results confirm both asymptotic stability and optimal tracking with an accuracy of 95%. The main contributions of this study include an optimal closed-loop controller with no mixed initial/final conditions, input/state constraints, asymptotic stability guarantee, a strong connection with geometric tools and finally the possibility to generalize to systems with multiple inputs. As a conclusion, general nonlinear control systems can be included in the optimal control methodology presented in this study including input/state constraints. Due to the lack of integral terms, the problem can be solved in closed form by using an optimal closed-loop controller.