Comparison of Nonlinear Filtering Methods for Battery State of Charge Estimation

• Main Author: Zhang, Klaus
• Format: Others
• Published: ScholarWorks@UNO 2014
• Subjects: nonlinear filtering; battery health management; state of charge estimation; Signal Processing; Systems and Communications
• Online Access: http://scholarworks.uno.edu/td/1896; http://scholarworks.uno.edu/cgi/viewcontent.cgi?article=2897&context=td

In battery management systems, the main figure of merit is the battery's SOC, typically obtained from voltage and current measurements. Present estimation methods use simplified battery models that do not fully capture the electrical characteristics of the battery, which are useful for system design. This thesis studied SOC estimation for a lithium-ion battery using a nonlinear, electrical-circuit battery model that better describes the electrical characteristics of the battery. The extended Kalman filter, unscented Kalman filter, third-order and fifth-order cubature Kalman filter, and the statistically linearized filter were tested on their ability to estimate the SOC through numerical simulation. Their performances were compared based on their root-mean-square error over one hundred Monte Carlo runs as well as the time they took to complete those runs. The results show that the extended Kalman filter is a good choice for estimating the SOC of a lithium-ion battery.