Work

Cooperative Estimation Method for SOC and SOH of Lithium-Ion Batteries Based on Fractional-Order Model

Year: 2025

Type: article

Abstract: To overcome the limitations of traditional integer-order models, which fail to accurately capture the dynamic behavior of lithium-ion batteries, and to improve the insufficient accuracy of state of charge (SOC) and state of health (SOH) collaborative estimation, this study proposes a cooperative estimation framework based on a fractional-order model. First, a fractional-order second-order RC equivalent circuit model is established, and the whale optimization algorithm is applied for offline parameter identification to improve model accuracy. Second, a strong tracking strategy is introduced into the improved unscented Kalman filter to address the convergence speed issue under inaccurate initial SOC conditions. Meanwhile, the extended Kalman filter is employed for SOH estimation and online parameter identification. Furthermore, a multi-time-scale collaborative estimation algorithm is proposed to enhance overall estimation accuracy. Experimental results under three dynamic operating conditions driving cycles demonstrate that the proposed method effectively solves the SOC/SOH collaborative estimation problem, achieving a mean SOC estimation error of 0.45% and maintaining the SOH estimation error within 0.25%.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj16090533

Authors: Guoping Lei, Tian-Ao Wu, Tao Chen, Juan Yan, Xiaojiang Zou

Institutions: Chongqing Three Gorges University, Yunnan Water Conservancy and Hydropower Vocational College

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: gold

Estimated APC paid: $1,689