This work presents a systematic review of the application of natural language processing (NLP) along the entire battery life cycle, instead of the review of one stage or method, and introduces a novel conceptual technical language processing (TLP) framework for the European Union (EU)'s proposed digital battery passport (DBP) and other general battery predictions, as the main objective. It follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and employs three reputable databases or search engines, including Google Scholar, Institute of Electrical and Electronics Engineers Xplore (IEEE Xplore), and Scopus, searched in October, 2025. The search criteria are 'text', 'language processing', and 'battery' while the exclusion criteria are non-relevant papers and those published before 2017, which was applied to all databases, except IEEE Xplore because it turned up fewer results. Consequently, it assesses 274 scientific papers before the critical review of the final 66 relevant papers. We publicly provide artifacts of the review for validation and possible 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. The proposed TLP framework, which incorporates agentic AI and optimized prompts, may be apt for tackling some of the challenges.

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