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

• Published in: International journal of energy research Vol. 46; no. 13; pp. 19190 - 19205
• Main Authors: Gu, Tianyu, Wang, Dongqing
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 25.10.2022
• Subjects: Accuracy; Algorithms; Antennae; Artificial neural networks; beetle antennae search (BAS); Delay lines; Dynamic characteristics; Intelligence; Learning algorithms; Machine learning; Methods; Model accuracy; Modelling; Neural networks; Neurons; recurrent extreme learning machine (recurrent‐ELM); State of charge; state of charge (SOC); Time lag
• ISSN: 0363-907X; 1099-114X
• DOI: 10.1002/er.8514

Summary 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. A Recurrent‐extreme learning machine (R‐ELM) model with time‐delay lines is investigated for online battery state of charge (SOC) estimation. The beetle antennae search (BAS) algorithm is adopted for searching optimal neuron number of hidden layer. The recursive least square (RLS) algorithm is adopted for optimal output layer weights training. The simulation results carried on the dynamic stress test (DST) and urban dynamometer driving Schedule (UDDS) datasets show the proposed method has great estimation performance.