An improved SOC estimation method based on noise-adaptive particle filter for intelligent connected vehicle battery

In order to effectively use the cloud data of connected vehicle to estimate the battery state of charge (SOC), an estimation method based on noise adaptive particle filter (N-APF) is proposed in this paper. Firstly, several cells are connected in series under the laboratory environment to simulate the grouping of battery packs in real vehicle. Besides, the federal test procedure (FTP) operating current for battery pack is obtained through software simulation combined with the actual vehicle parameters. Then, the Thevenin equivalent circuit model is established and the reliability of online identification of model parameters based on 10s interval data is verified. Furthermore, the effectiveness of the proposed noise adaptive particle filter method for adjusting the process noise and enhancing the stability of the SOC estimation is proved. Finally, the reliability of the improved SOC estimation method for the connected vehicle is verified based on the 10s interval cloud data, which shows the proposed noise adaptive particle filter estimation method can stabilize the SOC estimation error below 5% except for some high-current discharge phases.