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

2026-04-28

AI Summary

This work proposes a novel predictive framework that integrates battery degradation mechanisms with deep learning to enhance the reliability, safety, and operational efficiency of electric vehicles and large-scale energy storage systems. By combining a specialized neural network architecture, extensive battery operational data, and an end-to-end training strategy, the method accurately models capacity fade and state-of-health evolution. The resulting model significantly improves the accuracy of remaining capacity and lifetime predictions, enabling real-time health monitoring, predictive maintenance, and intelligent energy dispatch. This approach provides critical decision support for sustainable and intelligent 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.

Problem

• battery state prediction
• remaining capacity
• battery lifetime
• energy management
• health indicators

Innovation

• Deep Learning
• Battery State Prediction
• Degradation Modeling
• Intelligent Energy Management
• Predictive Maintenance