A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management
Accurate forecasting of battery health indicators, including remaining capacity and lifetime, is of paramount importance for ensuring the reliability, safety, and operational efficiency of applications such as electric vehicles and large scale energy storage infrastructures. The result of the forecasting can be adopted to build an advanced monitoring mechanism for continuous checking batteries' health status to assist in the efficient real-time management of numerous applications. This research investigates the development and implementation of a Deep Learning (DL) model for the prediction of the future state and performance of industrial electrochemical energy storage systems. To address...
| Authors | Athanasios Koukosias, Vasileios Tzanidakis, Sotiris Athanasiou et al. |
|---|---|
| Primary category | eess.SP |
| Published | 28 April 2026 |
| Last updated at source | 28 May 2026 |
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