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Tracing the technological development trajectory in post-lithium-ion battery technologies: A patent-based approach

阳极 电池(电) 锂(药物) 电解质 阴极 电化学 锂离子电池 材料科学 纳米技术 工艺工程 计算机科学 工程物理 化学 电气工程 功率(物理) 工程类 电极 物理 内分泌学 医学 物理化学 量子力学
作者
Lukas Jan Aaldering,Chie Hoon Song
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:241: 118343-118343 被引量:114
标识
DOI:10.1016/j.jclepro.2019.118343
摘要

With the ever-growing energy demands, new battery chemistries beyond lithium ion technology are required to deal with an increased power consumption and promote the vehicle electrification. However, there are still challenges in understanding the cognitive structure of evolving R&D processes. The study of the patent landscape can provide a complementary perspective on the trajectories of technological change and facilitate interdisciplinary discussions that help make future technology planning more rationally. In this study, we investigate the technological development trajectory of post-lithium-ion battery technologies based on the analysis of patent data. A data-driven approach, which comprises of patent co-classification analysis, application of link prediction algorithm and text mining technique, is used to highlight the recent research progress on selected post-lithium-ion battery technologies. These are lithium air, lithium sulfur and sodium-ion batteries. A lithium air battery is composed of a lithium metal anode, a porous carbon cathode with high catalytic activities towards electrochemical oxygen reactions, and either an aqueous or non-aqueous electrolyte. Similarly, a lithium sulfur battery consists of a lithium metal anode, a sulfur composite cathode, and an organic electrolyte. Sodium-ion batteries share the same architecture as the lithium ion-batteries, but are normally based on carbonaceous anode materials, layered transition metal oxides cathode demonstrating a minimal structural change upon intercalation, and aqueous electrolytes. The results indicate that the number of patents related to post-lithium-ion battery technologies has noticeably increased since 2008. A few East Asian countries and USA dominate the underlying patent landscape. The co-classification analysis showed that not only the amount of interacting knowledge areas but also the cross-linking with adjacent technological knowledge areas has increased. Moreover, the knowledge areas related to the electric vehicles and polymer applications are predicted to gain more relevance in the future. According to the text mining results, the most common application areas comprise vehicles, devices, energy storage, computers and phones. The main innovations of this study are as follows: First, it identified the current research trends and prospects for the post-lithium-ion battery technologies based on the insights gained from objective data, assisting R&D planners in determining their further directions for research and development. The exploration of the technological development trajectory of post-lithium-ion battery could serve as a reference for relevant researchers to better understand the dynamics and make strategic adjustments, which have profound significance for the progress of clean energy and green chemistry development. Second, it discovered the dynamics of interacting knowledge areas, which indicate an increased tendency of technology convergence. In case of lithium air and lithium sulfur batteries, notable interactions between knowledge areas pertaining to the core battery components (cell configurations and electrolytes) and application-driven knowledge fields (electric vehicles and phones) were observed. The intensity of these interactions was rather weak in case of sodium-ion battery technology. Accordingly, researchers need to seek for new knowledge outside of their traditional knowledge boundaries to accelerate the commercial breakthrough of post-lithium-ion batteries. In particular, knowledge areas related to electrical vehicles have gained in importance in all of the examined battery technologies, reflecting the drive to produce high-energy battery systems. Third, it predicted the knowledge areas that could gain more relevance in the future research by comparing the predictive power of different machine learning algorithms. This forward-looking approach can add a new empirical perspective to the discussion of sustainable technology development. Fourth, in contrast to common assumption, it showed that SIBs could also power up portable applications like mobile devices or electric vehicles.
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