Adaptive Online Gated Recurrent Unit for Lithium-Ion Battery SOC Estimation

Abstract

The Li-ion batteries are commonly used for Electric Vehicles (EVs) and aerospace applications. One of the essential parameters in Li-ion batteries is state of charge (SOC) that shows the available energy in a battery. Various methods were proposed for SOC estimation. Since the battery has a nonlinear equations, it is important to use a method that does not require the system model. In the present study, a new Adaptive Online Gated Recurrent Unit (GRU) method is proposed for the State of Charge (SOC) estimation. It is a kind of deep Recurrent Neural Network(RNN) which solved the vanishing gradient problem in RNNs with GRU units. For Optimization a robust adaptive Online gradient learning method is used. This method is able to tune online the learning rate in the process. Adaptive GRU is a nondependent method from the nonlinear batteries model and simplifies the mathematical computation. The proposed technique is implemented on the real dataset of LifePO4 Li-ion batteries for finding SOC estimation. The exprimental result indicate that the Adaptive GRU method is more accurate than simple RNN.