BKBAgent: A Battery Parameter Identification Agent with Bayesian Experimental Design and Knowledge Graph

Electrochemical battery models support battery design, state estimation, and control, but their accuracy depends on identifying internal parameters from operating measurements such as voltage. Existing work has applied large language model (LLM) agents to this task, using physical reasoning and simulation feedback to guide parameter updates. However, identifying coupled battery parameters from limited measurements is ill-posed: different parameter combinations can produce nearly identical responses. We introduce BKBAgent, a battery parameter identification agent that combines a physics-informed knowledge graph with Bayesian experimental design (BED). The agent maintains a joint belief over plausible parameter combinations and uses retrieved physical knowledge to propose candidates and anticipate their responses. Using these predictions, BED selects the next candidate by its expected change in the belief over parameter hypotheses. Simulator feedback updates the belief, and the agent retains parameter combinations whose simulated responses match the measurements. We evaluate BKBAgent on a benchmark comprising 108 test tasks with multi-parameter perturbations and large single-parameter shifts across operating rates. Under the same simulation budget, it reduces mean parameter identification error by 25.1% and 23.8% on the two task types, respectively, compared with an LLM-agent baseline. Ablations show that removing either retrieved physical knowledge or BED increases parameter identification error. These findings illustrate how structured domain knowledge and information-guided simulation can support scientific agents in reasoning over competing hypotheses and updating beliefs from feedback.