Sensorless battery expansion estimation using electromechanical coupled models and machine learning

This work introduces a method for estimating battery expansion using a data-driven approach combined with electromechanical coupled models. The method integrates a pressure-dependent impedance model and a data-driven mechanical model to enable sensorless estimation of battery expansion. By utilizing estimated SOC from reduced-order impedance models, current, and calibrated expansion stress, this approach highlights its potential for fault diagnosis based on expansion characteristics. Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring, improving the accuracy of digital twin simulation and abnormality detection. Therefore, this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning. The proposed method integrates reduced-order impedance models with data-driven mechanical models, coupling the electrochemical and mechanical states through the state of charge (SOC) and mechanical pressure within a state estimation framework. The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure. The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints. Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%. The results demonstrate the model’s estimation capabilities, achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa, outperforming calibrated stiffness models and other machine learning techniques. The model’s robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors. This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.