An Industrial Perspective and Intellectual Property Landscape on Solid-State Battery Technology with a Focus on Solid-State Electrolyte Chemistries
Bibliographic record
Abstract
This review focuses on the promising technology of solid-state batteries (SSBs) that utilize lithium metal and solid electrolytes. SSBs offer significant advantages in terms of high energy density and enhanced safety. This review categorizes solid electrolytes into four classes: polymer, oxide, hybrid, and sulfide solid electrolytes. Each class has its own unique characteristics and benefits. By exploring these different classes, this review aims to shed light on the diversity of materials and their contributions to the advancement of SSB technology. In order to gain insights into the latest technological developments and identify potential avenues for accelerating the progress of SSBs, this review examines the intellectual property landscape related to solid electrolytes. Thus, this review focuses on the recent SSB technology patent filed by the main companies in this area, chosen based on their contribution and influence in the field of batteries. The analysis of the patent application was performed through the Espacenet database. The number of patents related to SSBs from Toyota, Samsung, and LG is very important; they represent more than 3400 patents, the equivalent of 2/3 of the world’s patent production in the field of SSBs. In addition to focusing on these three famous companies, we also focused on 15 other companies by analyzing a hundred patents. The objective of this review is to provide a comprehensive overview of the strategies employed by various companies in the field of solid-state battery technologies, bridging the gap between applied and academic research. Some of the technologies presented in this review have already been commercialized and, certainly, an acceleration in SSB industrialization will be seen in the years to come.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".