Plantilla PFC

This thesis explores the modelling of lithium batteries and integrates it into Kalman filtering to estimate the state of charge (SOC) of lithium batteries. As Li-ion batteries are used in many applications, from cell phones to electric vehicles, battery management systems (BMS) that ensure their safe and efficient operation have become critical. Meanwhile, accurate SOC estimation is a prerequisite for efficiently operating a battery management system (BMS), and Kalman filtering provides an effective way to estimate SOC accurately. In this study, the battery is first physically modelled and identified the parameters; then by these parameters, the SOC is estimated by combining the model predictions with the measured values using Extender Kalman Filter (EKF); all simulation data are derived from test data from Prof. Phillip Kollmeyer's lab at McMaster University. The modelling results indicate that the Dual Polarization (DP) model describes the electrical behaviour of Li-ion batteries relatively accurately and that the model accuracy deteriorates as the temperature decreases and the terminal current rises. In addition, online parameter identification at room and high temperatures is more accurate, and offline parameter identification at low temperatures is more suitable; on the other hand, offline parameter identification is more appropriate for small current variation ranges, and online parameter identification is more suitable for large current variation ranges. Subsequent SOC estimation results show that the Extender Kalman filtering algorithm can combine the battery model and traditional algorithms well to enhance the adaptive and self-corrective enhancement of SOC estimation; similarly, the estimation accuracy decreases with decreasing temperature. The research results provide valuable practice and experience data in the theory of lithium battery modelling and SOC algorithms, which can help promote the development of lithium battery technology in commercial applications and improve the safety and economic benefits of battery use.