Combined estimation of the state of charge of a lithium battery based on a back-propagation- adaptive Kalman filter algorithm

The precise estimation of the battery's state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation-adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge.