This work presents the modeling and characterization of novel LFP-LTO lithium-ion cells with thick electrodes specifically designed for low-power applications, such as cardiac pacemakers. The selected cells, manufactured in a 2032 coin format with a nominal voltage of 1.9 V and a capacity of 15 mAh, were modeled using equivalent circuit models (ECMs), including both a basic first-order RC (1RC) and an enhanced three-RC-branch (3RC) Thevenin model. A multi-objective optimization strategy was employed to balance accuracy and computational complexity. To capture the dynamic behavior of the cells, a pulse-PRBS (Pulse-Pseudo-Random Binary Sequence) identification signal was applied, enabling precise parameter extraction over a broad frequency spectrum. Experimental results demonstrate that the 3RC model achieves voltage errors below 0.1%, making it suitable for accurate modeling under dynamic load conditions. Furthermore, the series configuration of two LFP-LTO cells reaches a nominal voltage of 3.7 V, matching the requirements of conventional pacemaker batteries. In addition, aging analysis was conducted by applying the same PRBS signal to the cells both before and after accelerated cycling and then modeling the results. These findings confirm that the 3RC model can also serve as a predictive tool for estimating battery aging and health. The proposed modeling methodology offers a reliable and efficient framework for integrating new thick-electrode technologies into biomedical applications.

Dual Active Bridge (DAB) converters have emerged as a leading solution for industrial applications that require high efficiency and bidirectional power transfer with isolation. The Triple-Phase-Shift (TPS) modulation scheme enables flexibility to enhance efficiency by choosing the operating point that minimizes power losses. This paper presents a novel, simplified methodology for reducing conduction losses in DAB converters using polynomial-based techniques. The operation of the DAB converter under TPS modulation is analyzed, and the corresponding modeling equations are derived. The proposed optimization method is described and validated through simulations in various design scenarios. The results demonstrate the existence of a set of three polynomials that effectively minimize the rms current based on the load conditions and the voltage ratio. Preliminary findings show that these polynomials will remain nearly constant across different converter designs, provided the output-to-input voltage ratio is the same.

In recent years the implementation of wireless power systems has increased due to improvements in technology and efficiency. Some advantages of wireless power transfer technology are reliability, safety, and convenience of use. One of the challenges with these systems is output regulation, as there are no physical wires to access it. Further complications are the variability of coil coupling and output power in the case of chargers. Firstly, this paper has summarized some of the main techniques for controlling the output of inductive power technology. Moreover, a predictive free communication control from the primary is proposed. It is aimed at charging systems with constant but unknown coupling, using a small drone charger as a reference. This control allows the secondary side to be simplified and acts faster than a control loop at the output due to the predictive measurement and feedforward operation. The simulation results for an output voltage of 5 V show an error of less than 3 % for a coupling range of 0.25 to 0.5.

This paper describes the analysis of a Dual Active Bridge converter (DAB) with soft switching and the minimum RMS current for the whole power range, using Triple Phase Shift (TPS) modulation. This analysis shows the conditions needed to have zero voltage switching (ZVS) and zero current switching (ZCS), furthermore the minimum RMS current, for all output power range. The validation has been done by a prototype rated for 250W.