Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering

Abstract

Summary Obtaining an accurate mapping relationship between the state‐of‐charge (SOC) and open‐circuit voltage (OCV) of lithium‐ion batteries at different ambient temperatures is of great significance for realizing accurate lithium‐ion battery SOC estimation considering the ambient temperature influence. However, the desired OCV‐SOC relationship is highly nonlinear, and the conventional polynomial fitting method is likely to result in relatively large fitting errors. To solve this problem, a method based on a backpropagation neural network (BPNN) to improve the OCV‐SOC fitting accuracy is proposed, and the SOC estimation of lithium‐ion batteries considering ambient temperature influence is completed. First, the relationship between the SOC and OCV of a lithium‐ion battery at different ambient temperatures is obtained by establishing a BPNN fitting model. Second, by optimizing the covariance decomposition process, a diagonalization of matrix unscented Kalman filtering (UKF) is proposed, which improves the accuracy and stability of the filtering algorithm. Then, the forgetting factor recursive least squares algorithm is combined to accomplish the online update of battery model parameters. Finally, under three working conditions, the effectiveness and robustness of the proposed method are verified. The simulation results show that the proposed method can obtain the most accurate SOC estimation results at each temperature, and the root mean square error (RMSE) and mean absolute error (MAE) under all three working conditions are less than 1.1%. Even if there is a certain error in the initial SOC, the proposed method can ensure that the RMSE and MAE of the SOC estimation results at each temperature do not exceed 1.5%.

A Composite State of Charge Estimation for Electric Vehicle Lithium-Ion Batteries Using Back-Propagation Neural Network and Extended Kalman Particle Filter

“…1, the parameters to be identified include R 0 , R 1 , R 2 , C 1 , C 2 . Recursive least squares algorithm with forgetting factors (FFRLS) 35,36 has the advantages of fast convergence, high accuracy, and adaptability, so it is employed to identify the battery parameter, the main steps are as follows:…”

An error covariance correction-adaptive extended Kalman filter based on piecewise forgetting factor recursive least squares method for the state-of-charge estimation of lithium-ion batteries

“…In recent years, the third category of methods has attracted a great deal of interest from many researchers [31], including Backpropagation Algorithm (BP) [32,33], Support Vector Machine (SVM) [34], Long Short-Term Memory (LSTM) [35,36], Gaussian process regression (GPR) [37], Multilayer Perceptron (MLP) [38], Hybrid Electrochemical-Thermal-Neural-Network model (ETNN) [39] and many others. These methods do not require consideration of the complex coupling relations between the estimated states and the influencing factors, and ignore the effects of model accuracy and noise characteristics [40].…”