Artificial Intelligence-Based Sustainability Assessment Framework for Lithium-Ion Batteries

Abstract

Lithium-ion batteries (LIBs) are crucial in electric vehicles and energy storage; however, their sustainability evaluation is constrained by methodological flaws. AI can bring revolutionary changes in the paradigm of lifecycle assessment (LCA) in LIBs. In this paper, SWOT analysis is used to evaluate the role and future potentials of AI in LCA in LIBs. The aim of this paper is to leverage the advantages of this technology while mitigating its shortcomings and evaluating possible opportunities and threats. Moreover, future research is suggested in order to improve the implementation of AI in the LCA of LIBs, such as the standardization of processes of data gathering, processing, and distribution, improving data transparency, fostering interdisciplinary cooperation and enhancing AI algorithms. This paper will contribute to the scientific discussion of the effective automation of sustainability evaluation in LIBs.

Introduction

The paper examines the integration of Artificial Intelligence (AI) with Life Cycle Assessment (LCA) to improve the sustainability evaluation of lithium-ion batteries (LIBs), which are widely used in electric vehicles (EVs), renewable energy systems, and grid-scale energy storage. Although LIBs play a vital role in reducing greenhouse gas emissions and supporting carbon neutrality, their production, use, and disposal raise significant environmental concerns, including resource depletion, energy-intensive manufacturing, carbon emissions, waste generation, and recycling challenges.

Life Cycle Assessment is a widely accepted method for evaluating the environmental impacts of LIBs throughout their entire life cycle—from raw material extraction and manufacturing to transportation, operation, recycling, and disposal. However, conventional LCA methods face several limitations, such as data scarcity, uncertainty, static assumptions, computational complexity, and difficulty adapting to rapidly evolving battery technologies and supply chains.

The paper highlights how AI, machine learning, deep learning, and large language models (LLMs) can overcome these limitations by processing large datasets, improving data quality, predicting battery performance and environmental impacts, automating life cycle inventory collection, optimizing recycling processes, and enabling real-time sustainability assessments. AI also supports decision-making by identifying environmentally preferable materials, manufacturing methods, and recycling strategies while improving resource efficiency and reducing carbon footprints.

The literature review shows that previous studies have identified battery chemistry, manufacturing location, electricity sources, and recycling processes as major factors influencing the environmental impact of LIBs. Recent research demonstrates that AI improves battery life prediction, intelligent energy management, recycling efficiency, and automated environmental data analysis. Nevertheless, researchers emphasize the need for better data quality, explainable AI, standardized databases, and interdisciplinary collaboration to ensure reliable AI-assisted sustainability assessments.

The study employs a SWOT (Strengths, Weaknesses, Opportunities, and Threats) framework to systematically evaluate AI's role in enhancing LIB life cycle assessment.

Strengths of AI include:

• Efficient processing of large and complex life-cycle datasets.
• Improved prediction of battery lifespan and environmental impacts.
• Automated life cycle inventory management using AI and LLMs.
• Faster, scalable, and more accurate sustainability assessments.
• Better optimization of battery operation, energy management, and recycling.

Weaknesses include:

• Heavy dependence on large, high-quality datasets.
• Limited transparency due to "black-box" AI models.
• Risk of overfitting and reduced generalization.
• Lack of standardized AI-LCA methodologies across the industry.

Threats involve:

• Data privacy and cybersecurity risks.
• Algorithmic bias leading to inaccurate or unfair conclusions.
• Ethical concerns in AI-based environmental decision-making.
• Uncertainty in model reliability due to biased or incomplete data.

Conclusion

The application of artificial intelligence technology in the sustainability assessment of LIBs can bring great possibilities for transforming the existing process of assessing the environmental impact of batteries. Even though there are some threats that should be faced, the opportunities created by AI-driven LCA for LIBs are quite significant. In future research, special attention should be paid to creating the unified standards for collecting, processing, and analyzing the data throughout the whole life cycle of LIBs and improving the quality of data. Moreover, the intersection of environmental science, data science, and engineering related to batteries is also required. The development of open, adaptive, and robust AI algorithms is crucial for achieving this goal.

References

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Copyright

Copyright © 2026 Avijit Chakraborty, Md Altab Uddin Molla, Subid Sundar Ghosh, Arnab Dutta, Dipasri Saha, Mintu Roy, David Das. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.