A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management

A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management

This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with...

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Bibliographic Details
Main Authors: Juliano Pimentel, Alistair A. McEwan, Hong Qing Yu
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
PHM
SOC estimation
Li-ion battery
data-driven
machine learning
bidirectional LSTM
Online Access:https://www.mdpi.com/2076-3417/15/15/8538
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