Authors: Gaurav Shinde, Rohan Mohapatra, Pooja Krishan, Harish Garg, Srikanth Prabhu, Sanchari Das, Mohammad Masum, Saptarshi Sengupta

Published on: March 28, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2403.19816

Summary

• What is new: It introduces a multidisciplinary approach to integrate Prognostics and Health Management (PHM) with Li-ion batteries, leveraging deep learning for enhanced prediction of Remaining Useful Life (RUL).
• Why this is important: The need for reliable, safe, and efficient lithium-ion batteries in various sectors.
• What the research proposes: A novel integration of PHM techniques, including deep learning models, to improve the prediction of RUL and thereby enhance battery safety and performance.
• Results: Demonstrated the effectiveness of deep learning techniques in accurately predicting RUL, leading to improvements in reliability and safety of Li-ion batteries.

Technical Details

Technological frameworks used: Deep learning architectures for RUL prediction

Models used: Deep Neural Networks

Data used: Battery usage and health data

Potential Impact

Energy storage technology, electric vehicle manufacturers, portable electronics, and renewable energy sectors could greatly benefit or face disruption due to these insights.

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