The Bidirectional Gate Recurrent Unit Based Attention Mechanism Network for State of Charge Estimation

Abstract

State-of-charge (SOC) plays an important role in the battery management system, and the accuracy of its estimation directly affects the efficiency and life of the lithium battery. In this paper, a bidirectional gate recurrent unit neural network based on the attention mechanism is proposed for SOC estimation. The nesterov adaptive momentum optimized algorithm is developed to update weight matrices of the neural network. This method has several advantages over the traditional methods and structures: (1) the proposed structure can well catch the dynamics of the SOC when compared with the traditional neural network structures; (2) the proposed algorithm has faster convergence rates than the momentum gradient descent algorithm. The simulation examples show the effectiveness of the proposed algorithm and structure.