Dublin, July 10, 2019 (GLOBE NEWSWIRE) -- The 'Lithium-Ion Battery High Energy Anode: Global Innovation & Patent Review Report 2019' report has been added to ResearchAndMarkets.com's offering. This review discusses options that are evaluated by key lithium-ion industry players to synthesize high energy negative electrode materials and corresponding electrodes, according to a machine learning-supported analysis of global patent filings.
Reasons to Buy
Comprehension of the high energy negative electrode decision tree allows for the identification of promising future R&D directions that have not yet been explored.
The review supports battery makers and automotive players in defining their roadmap, i. e. which anode materials can be used for mass applications at which energy density and with which timeline.
Key Highlights
The review highlights how innovation leaders combine many different process steps to obtain high performing materials and batteries. Many other players can learn based on this review which crucial parts of the innovation puzzle they have been considering to an insufficient extent thus far.
The authors of this review have prior hands-on' R&D and commercial experience in the Li-ion battery materials industry.
Scope
255,769 battery patent documents published across the globe between January 2017 and April 2019 have been screened using a machine learning approach (commercial relevance in the context of Li-ion battery anodes). The resulting ranking includes 296 companies. Patent portfolios by 34 key companies are discussed in detail and have been assembled into 17 decision trees that illustrate 106 different technical choices made by high energy material and Li-ion battery manufacturers. 3-5 key patents by another 51 companies are listed.
Key Topics Covered
Executive Summary About the Authors Introduction Focus of this Review Li-Ion Battery Cell Components Replacement of Graphite with Higher Energy Materials Decision Tree for High Energy Negative Electrodes Chemical Composition (Core) SiOX (0 < X < 2) - Synthetic Processes SiOX (0 < X < 2) - Coatings Lithiation of SiOX (0 < X < 2) Functionalization of Carbon-Coated SiOX (0 < X < 2) SiOX (0 < X < 2) Composites Nano-Si - Synthetic Processes Nano-Si - Coatings Coating of Carbon with Si Si-C Composites - Synthetic Processes Si-C Composites - Precursors Si-C Composites - Binders/Dispersants Si Alloys - Elemental Composition/Coatings Carbon Additives for Negative Electrodes Binders for Negative Electrodes High Energy Electrode Designs & Fabrication Methods Predictions Machine Learning-Based Identification of Commercially Relevant Patents Anode Material Suppliers Shin-Etsu - Japan Shanshan - China Hitachi/Maxell - Japan Datong Xincheng - China Kuraray - Japan BTR - China Mitsubishi Chemical - Japan Umicore - Belgium Showa Denko - Japan Wacker - Germany XFH - China Dongguan Kaijin - China Nanograf/SiNode/JNC - USA/Japan Posco - Korea Hunan Shinzoom/Hunan Xingcheng/Hunan Zhongke - China Shenzhen Sinuo - China 3M - USA BASF/enerG2/Toda Kogyo/Sion Power - Germany/USA/Japan IMERYS Graphite & Carbon - France/Switzerland Nexeon - Great Britain Sila Nanotechnologies - USA Paraclete (Kratos) - USA SJ Advanced Materials - Korea Elkem - Norway OneD Material - USA Lithium-Ion Battery Producers/Developers & Automotive Suppliers Toyota - Japan LG Chemical - Korea Hefei Guoxuan - China Samsung - Korea Panasonic/Sanyo - Japan Contemporary Amperex Technology Limited (CATL) - China BYD - China StoreDot - Israel Amprius - USA/China Additional Patent Filings with Commercial Relevance Patent Analysis Methodology & Validation List of Abbreviations
List of Figures Figure 1: Li-ion battery cell components Figure 2: decision tree - chemical composition (core) Figure 3: decision tree - SiOX (0 < X < 2) (synthetic processes) Figure 4: decision tree - SiOX (0 < X < 2) (coatings) Figure 5: decision tree - lithiation of SiOX (0 < X < 2) Figure 6: decision tree - functionalization of carbon-coated SiOX (0 < X < 2) Figure 7: decision tree - SiOX (0 < X < 2) composites Figure 8: decision tree - nano-Si (synthetic processes) Figure 9: decision tree - nano-Si (coatings) Figure 10: decision tree - coating of carbon with Si Figure 11: decision tree - Si-C composites (synthetic processes) Figure 12: decision tree - Si-C composites (precursors) Figure 13: decision tree - Si-C composites (binders/dispersants) Figure 14: decision tree - Si-C composites (coatings) Figure 15: decision tree - Si alloys (elemental compositions/coatings) Figure 16: decision tree - electrode formulation (carbon additives) Figure 17: decision tree - electrode formulation (binders) Figure 18: decision tree - electrode designs/fabrication methods Figure 19: projected manufacturing process for Shin-Etsu high capacity anode materials (1st part) Figure 20: projected manufacturing process for Shin-Etsu high capacity anode materials (2nd part) Figure 21:illustration of Si and SiO2 nano-domains in SiOX (X = 1) particles Figure 22: electrochemical bulk-reforming apparatus Figure 23: projected manufacturing process for Shanshan Si-C composites Figure 24: electrochemical data for Si-graphene-porous carbon compound (Shanshan) Figure 25: SEM and electrochemical characterization of Si-C composite (Shanshan) Figure 26: electrochemical cycling of graphene@SiO@Si compound (Shanshan) Figure 27: cycling stability of silicon-containing material (BTR) Figure 28: cycling stability of SiO-containing material (BTR) Figure 29: electrochemical cycling of Si-C composite (SiNode) Figure 30: pore size distribution and electrochemical data of ball milled Si (IMERYS) Figure 31: electrochemical cycling of polymer-coated Si particles (Nexeon) Figure 32: gradient Si-C composite (Sila Nanotechnologies) Figure 33: 1st cycle plot of pre-lithiated Si-based active material (Paraclete) Figure 34: CVD furnace design (OneD Material) Figure 35: bowl-shaped SiO2 particles (LG Chemical) Figure 36: electrochemical cycling of SiO-based active material (CATL) Figure 37: electrochemical cycling of SiO-C composite (BYD) Figure 38: C-Si-B anode material structure (StoreDot) Figure 39: design of S-shaped operating voltage window (StoreDot) Figure 40: SEM images of Si nanowires (top) and mixed Si/Cu nanowires (bottom) (Amprius)
List of Tables Table 1: precursors for Si-C composites Table 2: number of commercially relevant Li-ion battery anode patent families Table 3: number of commercially relevant patent families related to lithium metal containing batteries Table 4: optimization of Si/SiO2 nanostructure based on 29Si-MAS NMR measurements (Shin-Etsu) Table 5: optimization of Si domain size (Shin-Etsu) Table 6: electrochemical performance of silicon-based anode (Shanshan) Table 7: electrochemical performance of etched silicon-based anode material (XFH) Table 8: electrochemical data for Fe-Si alloys (3M) Table 9: electrochemical cycling data for milled Si/C (Nexeon) Table 10: electrochemical data for Si-C composite materials (Amprius)
Companies Mentioned
3M Amprius BASF BTR BYD CATL Datong Xincheng Dongguan Kaijin Elkem enerG2 Hefei Guoxuan Hitachi Chemical IMERYS JNC Kuraray LG Chemical Maxell Mitsubishi Chemical Nanograf Nexeon OneD Material Panasonic Paraclete Posco Samsung Shanshan Shenzhen Sinuo Shin-Etsu Shinzoom Showa Denko Sila Nanotechnologies SJ Advanced Materials StoreDot Toyota Umicore Wacker XFH
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