Author(s)

Anna Kowalski¹, Raj Patel^2*

Affiliation(s)

¹Warsaw University of Technology, Warsaw, Poland.

²Indian Institute of Technology Bombay, Mumbai, India.

Corresponding Author

Raj Patel

ABSTRACT

The integration of artificial intelligence into battery manufacturing represents one of the most significant technological advances in energy storage production of the past decade. This comprehensive study examines how AI is fundamentally transforming manufacturing processes, quality control, and materials innovation across the global battery industry. Through extensive analysis of implementation data from 150 manufacturing facilities spanning North America, Europe, and Asia, we demonstrate that AI integration has revolutionized production efficiency and quality control, leading to unprecedented improvements in manufacturing outcomes. Our findings, drawn from five years of operational data covering more than 500 million battery cells produced under AI-supervised conditions, reveal average productivity improvements of 35% and defect reduction rates of 45%. These results suggest that AI implementation is not merely an optimization tool but rather a fundamental paradigm shift in how advanced energy storage solutions are developed and manufactured.

KEYWORDS

Battery manufacturing; Machine learning; Quality control; Process optimization

CITE THIS PAPER

Anna Kowalski, Raj Patel. The role of artificial intelligence in advancing battery manufacturing: a comprehensive analysis. Journal of Manufacturing Science and Mechanical Engineering. 2024, 2(3): 1-5 DOI: https://doi.org/10.61784/msme3010.

REFERENCES

[1] Roman D, Saxena S, Robu V, et al. Machine learning pipeline for battery state-of-health estimation. Nature Machine Intelligence, 2021, 3(5): 447-456.

[2] Liu M, Ma Z, Li J, et al. Deep-Learning-Based Pre-training and Refined Tuning for Web Summarization Software. IEEE Access, 2024.

[3] Nozarijouybari Z, Fathy H K. Machine learning for battery systems applications: Progress, challenges, and opportunities. Journal of Power Sources, 2024, 601: 234272.

[4] Chen X, Liu M, Niu Y, et al. Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization. IEEE Access, 2024.

[5] Aykol M, Gopal C B, Anapolsky A, et al. Perspective—combining physics and machine learning to predict battery lifetime. Journal of The Electrochemical Society, 2021, 168(3): 030525.

[6] Ma Z, Chen X, Sun T, et al. Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization. Future Internet, 20024, 16(5): 163.

[7] Thomas J K, Crasta H R, Kausthubha K, et al. Battery monitoring system using machine learning. Journal of Energy Storage, 2021, 40: 102741.

[8] Sendek A D, Ransom B, Cubuk E D, et al. Machine learning modeling for accelerated battery materials design in the small data regime. Advanced Energy Materials, 2022, 12(31): 2200553.

[9] Wang X, Wu Y C, Ma Z. Blockchain in the courtroom: exploring its evidentiary significance and procedural implications in US judicial processes. Frontiers in Blockchain, 2024, 7: 1306058.

[10] Turetskyy A, Wessel J, Herrmann C, et al. Battery production design using multi-output machine learning models. Energy Storage Materials, 2021, 38: 93-112.

[11] Li J, Fan L, Wang X, et al. Product Demand Prediction with Spatial Graph Neural Networks. Applied Sciences, 2024, 14(16): 6989.

[12] Das K, Kumar R, Krishna A. Analyzing electric vehicle battery health performance using supervised machine learning. Renewable and Sustainable Energy Reviews, 2024, 189: 113967.

[13] Sun T, Yang J, Li J, et al. Enhancing Auto Insurance Risk Evaluation with Transformer and SHAP. IEEE Access, 2024.

[14] Wang X, Wu Y C. Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization. PloS one, 2024, 19(10): e0312623.

[15] Vidal C, Malysz P, Kollmeyer P, et al. Machine learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art. IEEE Access, 2020, 8: 52796-52814.

[16] Chen J, Cui Y, Zhang X, et al. Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach. Applied Sciences, 2024, 14(20): 9213.

[17] Zuo Z, Niu Y, Li J, et al. Machine learning for advanced emission monitoring and reduction strategies in fossil fuel power plants. Applied Sciences, 2024, 14(18): 8442.

[18] Heinrich F, Klapper P, Pruckner M. A comprehensive study on battery electric modeling approaches based on machine learning. Energy Informatics, 2021, 4(Suppl 3): 17.

[19] Wang X, Zhang X, Hoo V, et al. LegalReasoner: A Multi-Stage Framework for Legal Judgment Prediction via Large Language Models and Knowledge Integration. IEEE Access, 2024.

[20] Houchins G, Viswanathan V. An accurate machine-learning calculator for optimization of Li-ion battery cathodes. The Journal of Chemical Physics, 2020, 153(5).

[21] Finegan Donal P, Isaac Squires, Amir Dahari, et al. Machine-learning-driven advanced characterization of battery electrodes. ACS Energy Letters 7, 2022, 12: 4368-4378.

[22] Zhang X, Chen S, Shao Z, et al. Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning with Synthetic Pattern Generation. IEEE Open Journal of the Computer Society, 2024.

[23] Attia P M, Grover A, Jin N, et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature, 2020, 578(7795): 397-402.

[24] Moses I A, Joshi R P, Ozdemir B, et al. Machine learning screening of metal-ion battery electrode materials. ACS Applied Materials & Interfaces, 2021, 13(45): 53355-53362.

[25] Samanta A, Chowdhuri S, Williamson S S. Machine learning-based data-driven fault detection/diagnosis of lithium-ion battery: A critical review. Electronics, 2021, 10(11): 1309.

[26] Dave A, Mitchell J, Kandasamy K, et al. Autonomous discovery of battery electrolytes with robotic experimentation and machine learning. Cell Reports Physical Science, 2020, 1(12).