Scientometric research and critical analysis of battery state-of-charge estimation

Abstract

With the advent of lithium-ion batteries (LIBs) and electric vehicle (EV) technology, the research on the battery State-of-Charge (SoC) estimation has begun to rise and develop rapidly. In order to objectively understand the current research status and development trends in the field of battery SoC estimation, this work uses an advanced search method to analyse the literature in the field of battery SoC estimation from 2004 to 2020 in the Web of Science (WoS) database. We employed bibliometrics analysis methods to make statistics on the publication year, the number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of battery SoC estimation. With using the Citespace software, a total of 2946 relevant research literature in the field of battery SoC estimation are analyzed. The research results show that the publication of relevant research documents keeps increasing from 2004 to 2020 in the field of battery SoC estimation. The research topics focus on battery model, management system, LIB, and EV. The research contents mainly involve Kalman filtering, wavelet neural network, impedance, and model predictive control. The main research approaches include model simulation, charging and discharging data recording, algorithm improvement, and environmental test. The research direction is shown to be more and more closely related to computer science and even artificial intelligence (AI). Intelligence, visualization, and multi-method collaboration are the future research trends of battery SoC estimation.