Early-Stage State-of-Health Prediction of Lithium Batteries for Wireless Sensor Networks Using LSTM and a Single Exponential Degradation Model

Early-Stage State-of-Health Prediction of Lithium Batteries for Wireless Sensor Networks Using LSTM and a Single Exponential Degradation Model

One of the most critical items from the reliability and the State-of-Health (SOH) point of view of wireless sensor networks is represented by lithium batteries. Predicting the SOH of batteries in sensor-equipped smart grids is crucial for optimizing energy management, preventing failures, and extend...

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Bibliographic Details
Main Authors: Lorenzo Ciani, Cristian Garzon-Alfonso, Francesco Grasso, Gabriele Patrizi
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Sensors
Subjects:
machine learning
LSTM
recurrent neural networks
batteries’ state of health
NASA
single exponential model
Online Access:https://www.mdpi.com/1424-8220/25/7/2275
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