Research

Since the internal resistance of a battery changes with the state of health (SOH) of the battery, we analyze the aging state of the battery by calculating the change in internal resistance.

Our research involves experiments and theoretical studies to analyze how storing batteries for an extended period of time under varying environmental temperatures affects their cycle life.

By utilizing electrochemical governing equations, we create a model for short-circuited lithium-ion batteries (LIB) and employ heat equations to simulate the temperature distribution of thermally unstable batteries. This approach allows us to precisely anticipate the temperature fluctuations of the battery throughout and following the piercing process.

We perform axisymmetric mechanical analysis to investigate the layer-by-layer behavior of an artificial solid electrolyte interface (SEI) layer on the anode surface that arises from dendrite growth, as the layer can exert mechanical forces to prevent or inhibit the harmful growth of dendrites in lithium-ion batteries.

We propose a dynamic calculation method for estimating the open circuit voltage (OCV) of batteries using local voltage information during rest periods, enabling real-time estimation of the battery's state.

We employ adaptive extended Kalman filter (AEKF) and deep learning (DNN) techniques to estimate the state of health (SOH), state of charge (SOC), and battery temperature under dynamic current conditions.

We estimate the state of health (SOH) of batteries using a deep learning (DL) model and obtain real-time estimates by capturing a portion of the battery's current data starting from the constant voltage charging (CV) process.

We use the first derivative of discharge curves (dQ/dV) and discharge voltage as the objective function for electrochemical parameter estimation, and combine it with deep neural networks to achieve accurate and efficient parameter identification.