This review discusses technical options that are pursued by key commercial lithium-ion battery players to build solid-state Li-ion batteries for an increasing number of applications (IoT, medical devices, consumer electronics, electric vehicles/trains, stationary applications).
A machine learning supported screening of the global patent literature for commercial relevance provides the basis for unique insights, which have been condensed into an innovation decision tree and a gap analysis (liquid vs. solid electrolyte Li-ion batteries).
This review is based on a machine learning supported screening of 260,004 patent documents.
23 decision tree diagrams illustrate how R&D players have made a variety of choices as to which concepts, materials, processes, architectures to pursue.
The review includes a discussion of 11 current and 24 prospective solid-state lithium-ion battery suppliers, as well as of 6 materials & technology suppliers.
Key solid-state battery patent families by 38 additional companies are listed with links to the full text.
Reasons to Buy
Innovation decision tree diagrams allow for a comprehensive understanding as to how R&D decisions diverge or are similar between different protagonists. By understanding the weaknesses and strengths of different innovators, unique R&D programs can be defined that study unexplored areas based on a well-adjusted resource allocation that makes time-to-market targets achievable.
A key highlight is that this review condenses the global R&D effort in the area of solid-state Li-ion batteries into easy to understand graphs. Connections and divergences can be identified between players that are very different in geographical location or size.
A deep dive on cathode/solid electrolyte interface engineering options provides for inspiration as to how the longevity of solid-state Li-ion batteries can be further improved.
Key Topics Covered
1. Executive Summary
2. About the Author
Focus of this Review
Solid-State vs. Liquid Li-Ion Batteries
4. The Solid-State Li-Ion Battery Market Today
5. Battery Technology Adoption Framework
Application Requirements & Industrial Logic
Electronics - Integrated Circuits
Electronics - Mobile Computing
Automotive & Rolling Stock (Train) Applications
Patent Portfolio Readiness Level (PPRL)
Machine Learning-Based Identification of Commercially Relevant Patents