LAUSR

State of energy (SOE) is a critical index of lithium battery. The problem of the inaccurate available energy and recovered energy of lithium battery affects the accuracy of SOE estimation. In order to solve the problem, this paper proposes a method to estimate the available discharge energy of lithium batteries based on response surface model. In this method, the energy efficiency of lithium batteries in different states is obtained by establishing the relationship model of external charge voltage and external discharge voltage, so as to estimate the actual available energy of lithium batteries in different charge states. On this basis, a correction method based on radial basis function (RBF) neural network is proposed to estimate the actual energy released by the recovered energy when the current direction of the battery is changed. The proposed energy correction method is combined with the adaptive particle filter algorithm to estimate SOE. This method is not limited to the assumption of Gaussian function and can accurately predict the noise variance, so as to improve the estimation accuracy of SOE. Simulations under urban dynamometer driving schedule (UDDS) are conducted, and the result shows that the proposed method can effectively estimate the battery energy and improve the accuracy of SOE estimation.