Application of Unscented Kalman Filter in the SOC Estimation of Li-ion Battery for Autonomous Mobile Robot
When the Autonomous Mobile Robot(AMR) is popular in unknown environment, accurate estimation of SOC(State of Charge) is becoming one of the primary challenges in Autonomous Mobile Robots research. However, as defects of the Extended Kalman Filter(EKF) in nonlinear estimation, there exists estimated error, which affects the estimation accuracy , when it is adopted in nonlinear estimation of a battery system. In order to yield the higher accuracy of SOC estimation, a novel method-Unscented Kalman Filter (UKF) was employed in SOC estimation for a battery system. The EKF and UKF are compared through experiments. Experimental results show that the UKF is superior to the EKF in battery SOC estimation for AMR.
UKF Li-ion battery SOC EKF AMR.
Pu Shi Yiwen Zhao Pu Shi
Shenyang Institute of Automation Chinese Academy of Sciences Shenyang, 110016, China Graduate School Chinese Academy of Sciences China
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
1279-1283
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）