From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle

TL;DR

This paper systematically reviews NLP applications across the entire battery life cycle, introduces a new TLP framework for battery passports, and highlights emerging NLP tasks and challenges in the battery domain.

Contribution

It provides a comprehensive survey of NLP in battery lifecycle management and proposes a novel TLP framework for digital battery passports and related predictions.

Findings

Emerging NLP tasks facilitate materials discovery and lifecycle stages.

Challenges include lack of standard benchmarks.

The TLP framework incorporates agentic AI and optimized prompts.

Abstract

We present a comprehensive systematic survey of the application of natural language processing (NLP) along the entire battery life cycle, instead of one stage or method, and introduce a novel technical language processing (TLP) framework for the EU's proposed digital battery passport (DBP) and other general battery predictions. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and employ three reputable databases or search engines, including Google Scholar, Institute of Electrical and Electronics Engineers Xplore (IEEE Xplore), and Scopus. Consequently, we assessed 274 scientific papers before the critical review of the final 66 relevant papers. We publicly provide artifacts of the review for validation and reproducibility. The findings show that new NLP tasks are emerging in the battery domain, which facilitate materials discovery and other stages of the life cycle. Notwithstanding, challenges remain, such as the lack of standard benchmarks. Our proposed TLP framework, which incorporates agentic AI and optimized prompts, will be apt for tackling some of the challenges.