A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management

TL;DR

This paper presents a deep learning framework for accurately predicting battery health and performance, aiding real-time energy management in electric vehicles and energy storage systems.

Contribution

It introduces a novel neural network-based computational framework that models battery degradation dynamics using large-scale datasets.

Findings

DL model achieves high accuracy in battery state prediction

Framework supports predictive maintenance and energy resource optimization

Results demonstrate potential for sustainable energy management

Abstract

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 this challenge, we propose a dedicated computational framework that integrates advanced neural network architectures with large-scale training datasets, enabling precise modeling of batteries degradation dynamics and operational trends. The proposed approach provides a decision support mechanism for the optimal management of batteries facilitating both predictive maintenance and the efficient allocation of energy resources. Our findings highlight the potential of DL-based predictive modeling to significantly contribute to the advancement of sustainable and intelligent energy management systems.