Battery-Sim-Agent: Leveraging LLM-Agent for Inverse Battery Parameter Estimation

2026-05-28 · Source: Artificial Intelligence · Field: Science & Research — Artificial Intelligence & Machine Learning, Engineering & Applied Sciences · Depth: Expert, quick

Summary

Battery-Sim-Agent is a novel framework designed for inverse battery parameter estimation, addressing the challenge of parameterizing high-fidelity battery "digital twins." This system reframes the inverse problem, traditionally handled by sample-inefficient black-box optimization (BBO) algorithms, as a reasoning task. It is the first to integrate a Large Language Model (LLM) agent in a closed loop with a high-fidelity battery simulator. The agent emulates a human scientist's approach by interpreting multi-modal simulator feedback, formulating physically-grounded hypotheses for discrepancies, and suggesting structured parameter updates. On a diverse benchmark suite covering various battery chemistries and operating conditions, Battery-Sim-Agent significantly surpasses BBO baselines like Bayesian optimization in parameter identification accuracy. The framework also demonstrates capability in complex long-horizon degradation fitting and is validated on real-world battery datasets.

Key takeaway

For research scientists developing high-fidelity digital twins or tackling complex inverse problems, you should re-evaluate reliance on traditional black-box optimization. Battery-Sim-Agent demonstrates that LLM-agent frameworks, by reframing these as reasoning tasks, offer significantly improved accuracy and sample efficiency. Consider integrating LLM agents into your scientific discovery workflows to enhance parameter estimation and accelerate innovation in areas like battery chemistry.

Key insights

LLM agents can reframe inverse scientific problems as reasoning tasks, outperforming traditional black-box optimization.

Principles

• Inverse problems benefit from reasoning-based approaches.
• LLM agents can interpret multi-modal scientific data.
• Integrating physics knowledge guides parameter updates.

Method

An LLM agent works in a closed loop with a high-fidelity simulator, interpreting multi-modal feedback, forming hypotheses, and proposing structured parameter updates.

In practice

• Implement LLM agents for complex scientific inverse problems.
• Enhance battery digital twin accuracy via LLM reasoning.
• Explore LLM-driven optimization for material discovery.

Topics

• LLM Agents
• Battery Digital Twins
• Inverse Problems
• Parameter Estimation
• Scientific Discovery
• Black-box Optimization

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.