Welcome to "BattProDeep" – a specialized repository dedicated to advancing the prediction of lithium-ion battery aging through sophisticated probabilistic prediction models.
The following figure displays the details of BattProDeep:
This tool has been accepted to be presented at the NEIS 2024 conference. Considering this, the preprint version of the paper explaining this tool is available here.
If you want to use this tool in your academic work, please cite it as follows:
Heidarabadi H, Graner M, Hesse H. BattProDeep: A Deep Learning-Based Tool for Probabilistic Battery Aging Prediction. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-5bh40
- Innovative Modeling: Utilizing cutting-edge machine learning techniques to accurately model and forecast the aging process of lithium-ion batteries.
- Battery Health Insights: Aiming to provide valuable insights into battery health and lifespan, enhancing the reliability and efficiency of lithium-ion batteries in various applications.
- Data-Driven Approach: Emphasizing a data-centric methodology to understand and predict the intricate patterns of battery aging.
The primary objective of this repository is to develop a robust and accurate predictive model for lithium-ion battery aging. This project seeks to contribute to the sustainable and efficient use of lithium-ion batteries, a critical component in modern electronics, electric vehicles, and renewable energy systems.