Abstract

Based on an SOC (State of Charge) estimation system for Lithium-ion battery by EKF (Extended Kalman filter), we have improved the accuracy by dynamic adjusting of noises in the battery model. Firstly, the battery model and the SOC (State of Charge) estimation algorithm by EKF are explained. Then, static optimization for the process noise and observation noise in the battery model is discussed. Afterward, an adaptive noise tuning method is proposed and its accuracy is evaluated by some examinations. The error by the static method is 0.97% and 1.21% in different test patterns. The average of the SOC estimation errors with the adaptive noise tuning is 0.87% and 1.16%. Significant improvement of accuracy has been achieved.

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 call

Disclaimer: 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.