Predicting Remaining Discharge Time for Lithium-ion Batteries based on Differential Model Decomposition

被引：0

作者：

Lyu, Dongzhen ^{[1
]}

Zhang, Jinrui ^{[1
]}

Zhang, Bin ^{[2
]}

Yang, Tao ^{[3
]}

Xiang, Jiawei ^{[1
]}

机构：

[1] Wenzhou Univ, Sch Mech & Elect Engn, Wenzhou, Peoples R China

[2] Univ South Carolina, Dept Elect Engn, Columbia, SC USA

[3] Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Wuhan, Peoples R China

来源：

2023 IEEE 2ND INDUSTRIAL ELECTRONICS SOCIETY ANNUAL ON-LINE CONFERENCE, ONCON | 2023年

基金：

浙江省自然科学基金;

关键词：

Prognosis; SOC; RDT; Differential model decomposition; SOC; MANAGEMENT; DIAGNOSIS; PROGNOSIS; LIFE;

D O I：

10.1109/ONCON60463.2023.10431130

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents a method for estimating Remaining Dischargeable Time (RDT) with enhanced prognostic capabilities. The method introduces an innovative prognostic strategy to accurately predict RDT, utilizing the Differential Model Decomposition (DMD) method as the basis for its mathematical framework. The effectiveness of the proposed RDT prognostic method, equipped with built-in prognostic capabilities, is demonstrated through real battery degradation experiments. Finally, the performance of the RDT prognostic method is comprehensively assessed using various online prognostic evaluation metrics. The results indicate that the DMD approach improves the consistency of discharge patterns during long-term degradation.

引用

页数：5

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