基于UKF滤波的自主移动机器人锂电池SOC估计

准确估计剩余电量(state of charge,SOC)关系到自主移动机器人(AMR)的生存与安全,是AMR研究中所面临的主要挑战之一。针对广义卡尔曼滤波估计SOC的不足,本文给出基于无色卡尔曼滤波(UKF)估计AMR锂电池SOC的新方法。通过试验对UKF和EKF进行了比较。试验验证了同样条件下,UKF比EKF具有更好的滤波估计精度。

Accurate estimation of SOC(State of Charge) becomes one of the primary challenges in Autonomous Mobile Robot(AMR), which is essential to the safety and existence of AMR. In this paper, to overcome the weaknesses of EKF, a new estimation method based on UKF (Unscented Kalman Filtering) is applied to SOC calculation of Li-ion battery for AMR. Experiments are made to compare the new filter with the EKF. The result demonstrates that under the same conditions UKF(Unscented Kalman Filtering) has higher filtering accuracy.

国家863计划资助项目(2005AA404290-1)