A High-Efficiency Capacitor-Based Battery Equalizer for Electric Vehicles.

Technology in electric vehicles has increased substantially in the past decade. Moreover, it is projected to grow at record highs in the coming years since these vehicles are needed to reduce the contamination related to the transportation sector. One of the essential elements of an electric car is its battery, due to its cost. Batteries comprise parallel and series-connected cell arrangements to meet the power system requirements. Therefore, they require a cell equalizer circuit to preserve their safety and correct operation. These circuits keep a specific variable of all cells, such as the voltage, within a particular range. Within cell equalizers, capacitor-based ones are very common as they have many desirable characteristics of the ideal equalizer. In this work, an equalizer based on the switched-capacitor is proposed. A switch is added to this technology that allows the disconnection of the capacitor from the circuit. In this way, an equalization process can be achieved without excess transfers. Therefore, a more efficient and faster process can be completed. In addition, it allows another equalization variable to be used, such as the state of charge. This paper studies the operation, power design, and controller design of the converter. Moreover, the proposed equalizer was compared to other capacitor-based architectures. Finally, simulation results were presented to validate the theoretical analysis.

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review.

The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.