arXiv:2605.29560v1 Announce Type: new Abstract: Parameterizing high-fidelity "digital twins" of batteries is a critical yet challenging inverse problem that hinders the pace of battery innovation. Prevailing methods formulate this as a black-box optimization (BBO) task, employing algorithms that are sample-inefficient and blind to the underlying physics. In this work, we introduce a new paradigm that reframes the inverse problem as a reasoning task, and present Battery-Sim-Agent, the first framework to deploy a Large Language Model (LLM) agent in a closed loop with a high-fidelity battery simu
The increasing maturity and capabilities of large language models are enabling their application to complex scientific and engineering problems previously constrained by traditional optimization methods.
This development represents a significant step towards automating and accelerating the design and optimization of critical energy storage technologies, directly impacting the transition to renewable energy.
The inverse problem of battery parameter estimation shifts from black-box optimization to an LLM-driven reasoning task, potentially reducing development cycles and improving battery performance.
- · Battery manufacturers
- · EV sector
- · Renewable energy sector
- · AI software providers
- · Traditional battery R&D firms relying solely on legacy optimization methods
Faster and cheaper development of next-generation batteries with optimized performance.
Accelerated adoption of electric vehicles and grid-scale energy storage due to improved battery economics and capabilities.
Enhanced energy security and reduced reliance on fossil fuels, as battery technology becomes more sophisticated and accessible.
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Read at arXiv cs.AI