Real time estimation of battery SOC and autonomous charging strategy for dynamic energy storage charging robot with extended Kalman filter

With the continuous development of mobile robots, battery detection and power management have become the current main research directions for robots. In order to address the insufficient adaptability of traditional technologies in dealing with dynamic environments and achieve the collaborative management of battery health status and multi-battery systems, a real-time charging state estimation algorithm based on extended Kalman filtering is proposed. This method simulates the signal transmission process of a robot battery pack, and uses the capacitance index to denote the amount of stored charge of the battery in the charging state. By using the least squares method to fit the voltage and current change curve in the circuit, a forgetting factor is applied to weaken the filtering saturation in the calculation process. The dynamic change curve in the circuit is processed by the extended Kalman filter to achieve battery charging state prediction. The proposed method has a prediction accuracy of 97.34%-98.75% for battery charging status. Meanwhile, the proposed model is used to simulate the battery's recharge test, and the battery's charge retention rate is maintained well, which is 12.68%-30.04% higher than other algorithms. In the application process, the proposed method has high battery durability under the recharge strategy, with an improvement of 14.62% -28.98% compared to other algorithms. Therefore, the proposed model can effectively identify the charging status of the robot, plan the recharge path reasonably, and improve the service life of the battery.