基于FFRLS-AEKF的6轮足机器人电池SOC估计

In view of the problems of the six wheeled-legged robot, such as low estimation accuracy of state of charge (SOC) and low accuracy of battery model, an estimation algorithm based on forgetting factor-based recursive least squares (FFRLS) and adaptive extended Kalman filter (AEKF) was proposed. Firstly, the parameters of the power battery equivalent model were identified based on FFRLS algorithm. Secondly, AEKF was used to estimate SOC online and provide accurate open circuit voltage for parameter identification. Finally, taking lithium battery pack of the robot as an example, a validating experiment was carried out under dynamic stress test (DST) conditions. The results show that the algorithm can accurately estimate the SOC of power battery, and the relative error of SOC estimation is less than 2.5%.