Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries

TL;DR

This paper introduces a two-stage neural network framework for early and accurate prediction of remaining useful life in lithium-ion batteries, addressing limitations of previous methods by identifying the first prediction cycle and modeling degradation.

Contribution

The novel two-stage framework improves RUL prediction accuracy by determining the first prediction cycle and modeling degradation patterns post-FPC, enhancing battery management.

Findings

Outperforms conventional RUL prediction methods

Accurately identifies the first prediction cycle (FPC)

Demonstrates improved real-world applicability

Abstract

Early prediction of remaining useful life (RUL) is crucial for effective battery management across various industries, ranging from household appliances to large-scale applications. Accurate RUL prediction improves the reliability and maintainability of battery technology. However, existing methods have limitations, including assumptions of data from the same sensors or distribution, foreknowledge of the end of life (EOL), and neglect to determine the first prediction cycle (FPC) to identify the start of the unhealthy stage. This paper proposes a novel method for RUL prediction of Lithium-ion batteries. The proposed framework comprises two stages: determining the FPC using a neural network-based model to divide the degradation data into distinct health states and predicting the degradation pattern after the FPC to estimate the remaining useful life as a percentage. Experimental results demonstrate that the proposed method outperforms conventional approaches in terms of RUL prediction. Furthermore, the proposed method shows promise for real-world scenarios, providing improved accuracy and applicability for battery management.