Work

An improved SOC estimation method for lithium batteries based on dynamic graph convolutional neural network and bidirectional fusion Q-Learning framework

Year: 2025

Type: article

Abstract: With the popularization of new energy vehicles and renewable energy, accurate estimation of battery SOC is crucial for vehicle performance and safety. Traditional methods have problems such as inaccurate models, accumulated errors, and susceptibility to temperature effects. This article proposes an improved method for the fusion of dynamic GCN and BF-QL: dynamic GCN optimizes node weights through temperature decay sensing adjacency matrix, while BF-QL integrates SOC error and SOH decay reward signal to optimize feature selection and learning rate. The experimental results of HPPC, BBDST, and DST operating conditions show that the RMSE of the algorithm is 0.35 %, 0.86 %, and 0.52 %, respectively, with a maximum reduction of 87.7 % compared to BP and 71.9 % compared to EKF, verifying its high accuracy and convergence.

Source: e-Prime – Nexus of Electrical Electronic and Intelligent Engineering

DOI: 10.1016/j.prime.2025.101143

Authors: Qiao Lin, Shunli Wang, Sergiy Dubinin, Lichao Wu, Kaikai Wang, Penghao Gu

Institutions: Nanchong Vocational College of Science and Technology, Belarusian National Technical University

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Funder: National Natural Science Foundation of China