Work

A Novel Back Propagation Neural Network-square Root Cubature Kalman Filtering Strategy Based on Fusion Dual Factor Parameter Identification for State-of-Charge Estimation of Lithium-ion Batteries

Year: 2024

Type: conference-paper

Abstract: Accurate real-time estimation of the state-of-charge (SOC) of the battery is of great significance for promoting the development of electric vehicles. In this research, a novel back propagation neural network-square root cubature Kalman filtering (BPNN-SRCKF) strategy based on fusion dual factor parameter identification for SOC estimation of lithium-ion batteries is proposed. First of all, with the organic integration of the forgetting factor and memory length, a dual factor parameter identification algorithm is designed. Secondly, the square root filtering is incorporated into the CKF algorithm to speed up the operation and avoid filter divergence. Finally, a BPNN is introduced to improve the fault tolerance of the model. The results show that the mean absolute error and root mean square error range from 1.13%~1.28% under complex working conditions, which proves that the proposed strategy has high precision and good robustness.

DOI: 10.1109/neessc62857.2024.10733526

Authors: Xuntao Xu, Shunli Wang, Chao Wang, Carlos Fernandez, Frede Blaabjerg

Institutions: Mianyang Polytechnic, Inner Mongolia University of Technology, Southwest University of Science and Technology, Robert Gordon University, Aalborg University

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: green

Funders: Robert Gordon University, National Natural Science Foundation of China

Awards: G5200146413, G7081163933