State estimation of lithium polymer battery based on Kalman filter

Accurately estimating the state of charge (SOC) of batteries is of particularly important for real time monitoring and safety control in electric vehicles. Four aspects of efforts are used to promote the accuracy of SOC estimation. Firstly, the equivalent circuit model based on Thevenin model is established, and the parameters of the model are identified by the forgetting factor recursive least square method (FFRLS). Secondly, aiming at the nonlinear relationship between the open circuit voltage (OCV) and SOC, the neural network is proposed to fit OCV and SOC. Besides, an improved adaptive unscented Kalman filter is created in this paper. By using the residual innovation sequence (RIS) to adjust the fixed window in the adaptive algorithm, which can promote the accuracy of SOC estimation. Finally, the effectiveness of the proposed model is verified under dynamic cycles. The experimental results indicate that the proposed method can effectively improve the estimation accuracy of SOC.

Automated summary

The study proposed four efforts to improve the accuracy of SOC estimation, including establishing an equivalent circuit model, using a neural network to fit OCV and SOC, and creating an improved adaptive unscented Kalman filter. Experimental results showed that these methods effectively enhance the estimation accuracy of SOC.