Review of Key Technologies for Remaining Useful Life Prediction of Power Batteries

Jinbo Zhang; Rui Yang; Hang Qin

doi:10.54691/0xavvb04

Authors

Jinbo Zhang
Rui Yang
Hang Qin

DOI:

https://doi.org/10.54691/0xavvb04

Keywords:

Lithium-ion Battery; Remaining Useful Life Prediction; Power Battery; Data-driven; Battery Management System.

Abstract

As the core power source of new energy equipment, the accurate prediction of the Remaining Useful Life (RUL) of power batteries is of great significance for ensuring the operational reliability of equipment, optimizing the full-life cycle management of batteries and reducing operation and maintenance costs, and has become a research hotspot in the field of energy and power engineering. This paper systematically sorts out and summarizes the existing battery RUL prediction methods: first, from the perspective of mechanism models, it reviews the principles and application scenarios of three types of methods, namely electrochemical models, equivalent circuit models and empirical models; second, it introduces data-driven prediction methods, covering mainstream algorithms such as filtering technology, Gaussian process regression and neural networks; third, it discusses fusion-based prediction strategies, including two core forms: the integration of mechanism models with data-driven methods, and the combination of multiple data-driven methods; finally, it summarizes the advantages and disadvantages of various methods, analyzes their application status and development trends in power battery management systems, and prospects the evolution path of future RUL prediction technology toward intellectualization, lightweight design and high precision, aiming to provide a reference for relevant research on power battery health management.

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