Abstract
This paper proposes a Kalman filter based state-of-charge (SOC) estimation MATLAB function using a second-order RC equivalent circuit model (ECM). The function requires the SOC-OCV (open circuit voltage) curve, internal resistance, and second-order RC ECM battery parameters. Users have an option to use an extended Kalman filter (EKF) or adaptive extended Kalman filter (AEKF) algorithms as well as temperature dependent battery data. An example of the function is illustrated using the LA92 driving cycle of a Turnigy battery performed at multiple temperature ranging from −10°C to 40°C.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
