Indirect Pressure Measurement in Batteries and State Estimation of SoH - Internal Gas Pressure measurement in Li-ion Cells for SoH Estimation using Deep Neural Network - Sunku, Aditya (My University, 2024)
Abstract This thesis presents a new methodology for internal gas pressure measurement in Li-ion batteries to predict the State of Health of the battery. This is achieved by a simulation study, experimental testing of Li-ion batteries and a Deep Neural Network (DNN) model for state estimation. The primary motive of this thesis is to measure the internal gas pressure in Li-ion batteries in a non-invasive approach and to asses the battery health based on the amount of gas generated inside the cell over its life cycle. The traditional methods used in present day BMSs, use electrical parameters like voltage and current as input parameters to asses the states of a battery like State of Health (SoH), State of Charge (SoC). This study aims to take into consideration the mechanical parameters of the battery like internal gas pressure and cell temperature, for state estimation. This Master Thesis deals with an in-operando, non-invasive pressure measurement of batteries. The internal gas pressure of the battery was monitored by capturing/measuring the strain developed on the battery cell lid. Uses this measured/predicted internal gas pressure as an input variable to the battery management system (BMS) for SoH estimation. Extensive cell testing was performed to capture the cell behaviour during cycling and measure the gas pressure developed inside the cell. This data was used as input parameters for the Deep Neural Network (DNN) model, developed to predict the State of Health (SoH) of the battery. Through this study, a correlation between internal gas pressure and health indicators of batteries like Direct Current Internal Resistance (DCIR), Discharge Energy and & Charge Capacity of the battery was established. This correlation further aids in accurately predicting the State of Health (SoH) of the battery.
Keywords: Keywords: Indirect gas pressure measurement, in-operando, gas pressure, strain, State of Health (SoH), BMS, Deep Neural Network (DNN), DCIR, Discharge Energy, Charge Capacity