Sera: Semantic Representation Aggregation for Reliable and Interpretable Battery Health Forecasting

Sera is a framework that augments traditional temporal models for battery state‑of‑health forecasting with semantic representations of degradation. It extracts degradation semantics from time‑series data using rule‑based knowledge and large‑language‑model interpretation, then encodes and gates these representations with the temporal model’s output. Experiments on a benchmark dataset show that Sera reduces prediction error by up to 37.3% and improves generalizability, while counterfactual analysis confirms that the model’s responses align with the meanings of key degradation descriptors.

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• llms
• benchmarks
• safety