基于数据驱动的锂离子电池剩余寿命预测综述

Yin Li; Jianfeng Wang; Weiquan Mo; Xili Zhang

doi:10.13234/j.issn.2095-2805.2025.7.253

基于数据驱动的锂离子电池剩余寿命预测综述

Translated title of the contribution: Review of Data-driven Remaining Useful Life Prediction for Lithium-ion Batteries

Yin Li
, Jianfeng Wang^*
, Weiquan Mo
, Xili Zhang

^*Corresponding author for this work

China Jiliang University
Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province

Research output: Contribution to journal › Review article › peer-review

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to the safe and reliable operation of new energy vehicles. First, the research status of data-driven methods for predicting the RUL of lithium-ion batteries is analyzed in this paper, and the research progress in six commonly used data-driven methods is reviewed. Then, three problems existing in the practical applications of RUL prediction of lithium-ion batteries at present are summarized. At the same time, the issue of battery dataset collection is discussed comprehensively, and the importance of battery datasets to the development of data-driven methods is also elaborated upon. Finally, the development trend in the future is prospected.

Translated title of the contribution	Review of Data-driven Remaining Useful Life Prediction for Lithium-ion Batteries
Original language	Chinese (Traditional)
Pages (from-to)	253-265
Number of pages	13
Journal	Journal of Power Supply
Volume	23
Issue number	7
DOIs	https://doi.org/10.13234/j.issn.2095-2805.2025.7.253
State	Published - 30 Nov 2025
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.13234/j.issn.2095-2805.2025.7.253

Cite this

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title = "基于数据驱动的锂离子电池剩余寿命预测综述",

abstract = "Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to the safe and reliable operation of new energy vehicles. First, the research status of data-driven methods for predicting the RUL of lithium-ion batteries is analyzed in this paper, and the research progress in six commonly used data-driven methods is reviewed. Then, three problems existing in the practical applications of RUL prediction of lithium-ion batteries at present are summarized. At the same time, the issue of battery dataset collection is discussed comprehensively, and the importance of battery datasets to the development of data-driven methods is also elaborated upon. Finally, the development trend in the future is prospected.",

keywords = "Lithium-ion battery, battery dataset, capacity regeneration, remaining useful life (RUL) prediction",

author = "Yin Li and Jianfeng Wang and Weiquan Mo and Xili Zhang",

year = "2025",

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doi = "10.13234/j.issn.2095-2805.2025.7.253",

language = "繁体中文",

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T1 - 基于数据驱动的锂离子电池剩余寿命预测综述

AU - Li, Yin

AU - Wang, Jianfeng

AU - Mo, Weiquan

AU - Zhang, Xili

PY - 2025/11/30

Y1 - 2025/11/30

N2 - Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to the safe and reliable operation of new energy vehicles. First, the research status of data-driven methods for predicting the RUL of lithium-ion batteries is analyzed in this paper, and the research progress in six commonly used data-driven methods is reviewed. Then, three problems existing in the practical applications of RUL prediction of lithium-ion batteries at present are summarized. At the same time, the issue of battery dataset collection is discussed comprehensively, and the importance of battery datasets to the development of data-driven methods is also elaborated upon. Finally, the development trend in the future is prospected.

AB - Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to the safe and reliable operation of new energy vehicles. First, the research status of data-driven methods for predicting the RUL of lithium-ion batteries is analyzed in this paper, and the research progress in six commonly used data-driven methods is reviewed. Then, three problems existing in the practical applications of RUL prediction of lithium-ion batteries at present are summarized. At the same time, the issue of battery dataset collection is discussed comprehensively, and the importance of battery datasets to the development of data-driven methods is also elaborated upon. Finally, the development trend in the future is prospected.

KW - Lithium-ion battery

KW - battery dataset

KW - capacity regeneration

KW - remaining useful life (RUL) prediction

UR - https://www.scopus.com/pages/publications/105026862152

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DO - 10.13234/j.issn.2095-2805.2025.7.253

M3 - 文献综述

AN - SCOPUS:105026862152

SN - 2095-2805

VL - 23

SP - 253

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JO - Journal of Power Supply

JF - Journal of Power Supply

IS - 7

ER -

基于数据驱动的锂离子电池剩余寿命预测综述

Abstract

UN SDGs

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