The study of edge-end collaborative management system for lithium batteries

For the management of an electric bicycle power battery pack, a lithium battery management system with a mobile application and collection terminal is designed to realize real-time monitoring of single battery voltage, current, and temperature. A recursive least squares algorithm with a forgetting factor (FFRLS) is used on the Android app for online parameter identification to solve the balance between accuracy and real-time SOC estimation. An extended Kalman filter algorithm (EKF) based on the equivalent circuit model with real-time parameter update is used on the acquisition terminal for real-time SOC estimation. The above functions and SOC estimation methods were tested and validated using battery test equipment. The results show that the accuracy of the selected SOC estimation algorithm is significantly higher than that of the ampere-time integration method.