A review of lithium-ion battery state of health and remaining useful life estimation methods based on bibliometric analysis - Xu Lei; Fangjian Xie; Jialong Wang; Chunling Zhang (Journal of Traffic and Transportation Engineering (English ed. Online))
In recent years, research on the state of health (SOH) and remaining useful life (RUL) estimation methods for lithium-ion batteries has garnered significant attention in the new energy sector. Despite the substantial volume of annual publications, a systematic approach to quantifying and analyzing these contributions is lacking. This study focuses on selecting pertinent literature related to lithium-ion battery SOH and RUL estimation from CNKI and WOS databases, spanning January 2010 to December 2023. Employing bibliometric tools such as VOSviewer and CiteSpace, we conduct visual analyses to elucidate the current state, development trends, and research frontiers in this domain. Our examination encompasses scholarly activity, year-wise literature distribution, international collaboration networks, structural dissemination, and journal contributions. The findings indicate an upward trend in annual publication output, with China, the United States, the United Kingdom, and Canada at the forefront of collaborative research efforts. China is increasingly recognized as a pivotal hub for global scholarly partnerships. Notably, Harbin Institute of Technology, Beijing Institute of Technology, Chongqing University, Chinese Academy of Sciences, and Beijing Jiaotong University are the top institutions in China and the world in terms of publications. The Journal of Energy Storage emerges as a prominent periodical, acclaimed both domestically and internationally for its rigorous standards and high-quality articles. Based on the research content from CNKI and WOS, VOSviewer clusters the main research directions into three themes: aging mechanisms, SOH estimation methods, and RUL prediction methods. Keywords such as ‘online estimation’, ‘hybrid models’, and ‘artificial neural networks’ feature prominently, signaling a strong emphasis on artificial intelligence strategies. The study concludes with a prospective outlook on imminent research trajectories regarding the health and longevity estimations of lithium-ion batteries, highlighting the critical need for ongoing innovation and collaboration in this essential field.