Comparison of battery charge times and stabilities using Pİ, SVPWM timer, and different lyapunov algorithms

The usage area of lithium-ion batteries is increasing rapidly. Additionally, with the developing technology, higher capacity, and faster charging systems have been needed. Control methods previously achieved on other systems have adapted to newly developed systems throughout the historical process. Based on this, a new timer has been added to the PI algorithm, which was already successful in the battery charging systems. This timer was implemented in the battery with the help of the SVPWM technique, which is extremely popular in electric motor drive circuits. The system's behaviour has been compared with different stability theories of Lyapunov. One issue with the battery management system is how to charge the battery. The old charging circuits are mostly electronic based and have less software, but today there is a demand for a healthier fast charging method using mathematical control algorithms to reduce the losses on the circuit elements and to obtain smaller charging circuits. In this study, in addition to the bidirectional converter circuit and PI algorithm previously used for the charging circuits, the SVPWM technique has been used in the charging circuit for high power transfer by taking advantage of the similarity between the motor driver circuits and the battery charging circuits. Additionally, this technique reduces the current over time to optimize the strains on the battery as the battery approaches full charge. Then, the Lyapunov stability algorithms have been added to the system, and the stability states were compared with each other. These comparisons were handled with graphs of current, voltage, SoC, and temperature against time. It has been shown in the simulation that the control algorithms make the system more stable by reducing the fluctuations in current and voltage. The topology that reaches the recommended current and voltage amount of the charged battery in the earliest time reaches the least amount of fluctuation and causes the least heating of the battery will be a more successful method. It is possible to comment on the SoH when the battery parameters are known. When new methods have added to the system, the battery's internal resistance change rate is slower and healthier. The health interpretation has been evaluated not only using the Kalman filter but also using the essential parameters of the control algorithms. The system stability was investigated using significant control parameters such as settling time and overshoot. Comments have been made on how this study could be improved.