The series connection of insulated gate bipolar transistor (IGBT)/diode devices allows the operation at voltage levels higher than the rated voltage of one IGBT/diode. However, due to individual parameter differences of the series-connected IGBT/diodes, it is difficult to ensure a proper voltage balance between them, and transient or steady-state voltage unbalances could cause the failure of these devices. This paper presents an active gate driver developed by the authors that is suitable for the series connection of IGBTs. The proposed active gate driver achieves the transient and steady-state voltage balance between the series-connected IGBT/diode devices. The effectiveness of the gate driver and the active gate control method has been experimentally validated, and promising results have been obtained.

Temperature is one of the key factors when working with lithium-ion battery modules due to its influence in safety, performance, and lifespan concerns in these devices. High working temperatures reduce the available capacity of each cell within the module after several cycles due to aging; nonuniform temperature distributions in the module lead to different aging processes in each cell, and thus it will be impossible to take advantage of all the energy available in the Li-ion storage system, because the most aged cell will limit the available energy. This paper is focused on the minimization of the maximum temperature gradient of a battery module for high depth of discharge applications to avoid different aging processes of the cells within the module. Voltage balancing is proposed as a solution for this purpose, and different strategies and their results are presented in this paper.

This paper presents a reduced-order electrochemical battery model designed for the online implementation of battery control systems. The model is based on porous-electrode and concentrated-solution theory frameworks and is able to predict voltage as well as the internal electrochemical variables of a battery. The reduction of the model leads to a physics-based one-dimensional discrete-time state-space reduced-order model (ROM), which is especially beneficial for online systems. Models optimized around different operational setpoints are combined to predict cell variables over a wide range of temperatures and state of charges (SOCs) using the output-blending method. A sigma-point Kalman filter is further used to manage inaccuracies generated by the reduction process and experimental-related issues such as measurement error (noise) in the current and voltage sensors. The state-estimation accuracies are measured against a full-order model (FOM) developed in COMSOL. The whole system is able to track the internal variables of the cell, as well as the cell voltage and SOC with very high accuracy, demonstrating its suitability for an online battery control system.

High power battery systems are composed of big array of series connected single cells. The main reason of increasing battery pack voltage is, to increase power by decreasing current and improve the efficiency. To control the charge and voltage of each cell a BMS with balancing system is necessary. Battery modeling is hard work, and battery dispersion and deviations are not taken into account. To overcome this problem, in this work a passive balancing system design procedure is presented based on single cell experimental tests. Battery modeling is avoided and main deviations between batteries are taken into account. Main parameters are taken into account during the design procedure. Finally design procedure is compared with experimental results.

Creating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algorithm learns the relationship between the different inputs and the output. Obtaining such data through laboratory tests is costly and time consuming; therefore, in this article, a neural network has been trained with data generated synthetically using electrochemical models. These models allow us to obtain relevant data related to different conditions at a minimum cost over a short period of time. By means of the different training rounds carried out using these data, it has been studied how the different hyperparameters affect the behaviour of the algorithm, creating a robust and accurate algorithm. To adapt this approach to new battery references or chemistries, transfer learning techniques can be employed.