Estimation of the Non-Measurable State Variables of a Transcutaneous Energy Transmission System for Artificial Human Implants Using Extended Kalman Filters

Due to the separation of the two sides of the coupling network, the acquisition of data on the operating state variables of a transcutaneous energy transmission system (TETS) inside the human body is difficult. A non-measurable state estimation approach is used in this work to facilitate the estimation of non-measurable variables on the secondary side of the TETS, including the current of the secondary coil and the output voltage of the secondary side. The estimation algorithm is based on a discrete dynamic mathematical model of the TETS. Following this model, using the extended Kalman filter (EKF) algorithm, the complexity of the TETS for artificial human implants can be reduced, while the reliability is simultaneously enhanced. Additionally, as an adaptive filter, the EKF can also successfully filter out processing noise during energy transmission. All of the results are verified by simulation using MATLAB.