A beetle antennae search optimized recurrent extreme learning machine for battery state of charge estimation

摘要

The extreme learning machine (ELM) is a single-hidden layer feedforword neural network (FNN) without training hidden layer weights/biases. By constructing a recurrent extreme learning machine (Recurrent-ELM) with time delay lines to model battery dynamic characteristics, a beetle antennae search based recursive least squares (BAS-RLS) method is explored to online realize the state of charge (SOC) estimation. The contents include: (1) To decrease the computational burden, the ELM model with fixed hidden layer weights is adopted to model battery SOC, and a RLS algorithm is studied to online estimate SOC by using the sampled terminal voltages and currents; (2) To solve the modeling accuracy problem, a Recurrent-ELM model with past/present voltages and currents, and past SOC as inputs is constructed by adding time delay lines to capture battery dynamic characteristics, so as to promote the battery modeling accuracy; (3) To determine suitable neuron numbers in the hidden layer, the BAS method is introduced to find the optimal neuron number in the hidden layer to promote intelligence of the Recurrent-ELM based RLS algorithm. Simulation results indicate that the proposed model and method has high precision in SOC estimation compared with traditional method.