학술논문

Predicting Remaining Discharge Time for Lithium-ion Batteries based on Differential Model Decomposition
Document Type
Conference
Source
2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON) On-Line Conference (ONCON), 2023 IEEE 2nd Industrial Electronics Society Annual. :1-5 Dec, 2023
Subject
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Degradation
Measurement
Lithium-ion batteries
Predictive models
Mathematical models
Discharges (electric)
Prognostics and health management
Prognosis
SOC
RDT
Differential model decomposition
Language
Abstract
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.