Human-centered battery twins

BAT-GPT

Can a battery digital twin explain its condition in language a user can act on?

Published in the 2025 IEEE ICUFN proceedings

Why I built it

How can a parameter-efficient language model make battery digital-twin insights more interactive and understandable?

Battery twins produce technical measurements and predictions, but their value depends on whether engineers and asset owners can interpret them. BAT-GPT explores a conversational layer that translates battery operating data into direct, domain-specific responses.

The project fine-tunes FLAN-T5 with Low-Rank Adaptation using battery conversations derived from NASA data. Zero-shot, few-shot, and parameter-efficient fine-tuning experiments reveal both the promise and the current limitations of language models as digital-twin assistants.

BAT-GPT model-in-the-loop battery digital twin and language assistant architecture

The complete BAT-GPT architecture links a capacity-predicting model-in-the-loop digital twin to prompt-response generation, FLAN-T5 adaptation, real-time battery data, and an interactive response loop.

How the system works

Battery conversations

Battery cycles, current, temperature, voltage, and capacity are transformed into prompt-response examples grounded in digital-twin data.

LoRA fine-tuning

FLAN-T5 is adapted to the battery domain through low-rank parameter updates rather than full-model retraining.

Prompting and evaluation

Base, zero-shot, few-shot, and fine-tuned responses are compared using human baselines and ROUGE metrics.

4ROUGE measures reported

0.298LoRA-FLAN-T5 ROUGE-L

0.292LoRA-FLAN-T5 ROUGE-Lsum

LoRAparameter-efficient adaptation

What this work contributes

From a research question to an inspectable system.

A domain-specific conversational assistant for battery digital twins.
A prompt-response pipeline grounded in battery operating cycles and environmental conditions.
Parameter-efficient adaptation of FLAN-T5 using LoRA.
Comparison of base, zero-shot, few-shot, and fine-tuned behavior.
An honest evaluation that documents improved coherence alongside remaining numerical errors.

Battery dataset prompt generation and digital twin conversation example — Battery measurements are converted into grounded prompt-response pairs, then used to teach the assistant how to explain capacity under specific current, temperature, cycle, and operating conditions.

How I read the results

What the evidence means

BAT-GPT is valuable because it tests the interface between numerical prediction and human understanding. The evaluation does not hide failure: some responses become clearer after adaptation while capacity estimates can remain inaccurate. That evidence defines where grounding and validation must improve.

Current scope

What this study validates

This study is an early language-interface prototype built around NASA battery data and FLAN-T5. It establishes the conversational component later integrated into the broader BatteryMetrix vision.

Inside the system

More evidence from the research.

FLAN-T5 encoder-decoder architecture used by BAT-GPT — The FLAN-T5 encoder-decoder architecture provides the language backbone, using attention and feed-forward blocks to transform battery prompts into contextual responses.

Low-rank adaptation method used to fine-tune BAT-GPT — LoRA freezes the pretrained language-model weights and learns compact low-rank update matrices, making domain adaptation more parameter efficient.

Battery digital twin interface with conversational assistant panel — The prototype places an assistant beside the battery twin so users can query model results within the monitoring environment.

Comparison of baseline human, original FLAN-T5, and parameter-efficient model responses — A response comparison shows how the base and parameter-efficient models interpret a battery-capacity prompt against a human reference.

Zero-shot FLAN-T5 response to a battery operating-condition prompt — The zero-shot example exposes repetition and weak physical interpretation, documenting the limitations that motivate domain-specific adaptation.