My research focuses on battery management systems (BMS), with particular emphasis on state-of-charge (SOC) estimation of electrochemical cells. I specialize in combining data-driven approaches (e.g., neural networks such as LSTM) with model-based methods (Kalman and Particle filters) to improve accuracy and robustness in real-time SOC estimation. I also work on the translation of machine learning models into embedded C code for deployment on microcontrollers, enabling practical implementation of advanced estimation techniques in real-world systems. My interests extend to smart energy systems, electric vehicle technologies and embedded systems for battery monitoring and control.