Estimation of Lithium-Ion Battery SOC Based on IFFRLS-IMMUKF

被引:0
作者
Zhao, Xianguang [1 ]
Wang, Tao [1 ]
Li, Li [1 ]
Cheng, Yanqing [2 ]
机构
[1] Hebei Univ Engn, Coll Mech & Equipment Engn, Mech Engn, New Campus, Handan 056038, Peoples R China
[2] Tianneng Battery Grp Co Ltd, Huzhou 313100, Peoples R China
来源
WORLD ELECTRIC VEHICLE JOURNAL | 2024年 / 15卷 / 11期
关键词
state of charge; Golden Jackal optimization algorithm; forgetting factor recursive least squares; interactive multiple model unscented Kalman filter;
D O I
10.3390/wevj15110494
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation can ensure the safety and reliability of vehicles. To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the Golden Jackal optimization (GJO) algorithm with the traditional FFRLS method. This integration is grounded in the formulation of a lithium battery state equation derived from a second-order RC equivalent circuit model. Additionally, the research utilizes the interactive multiple model unscented Kalman filter (IMMUKF) algorithm for SOC estimation, with experimental validation conducted under various conditions, including hybrid pulse power characterization (HPPC), urban dynamometer driving schedule (UDDS), and real underwater scenarios. The experimental results demonstrate that the SOC estimation method of lithium batteries based on IFFRLS-IMMUKF exhibits high accuracy and a favorable temperature applicability range.
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页数:23
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