A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

IEEE Access

Michael J. Ryan¹  Sajal K. Das²  Md H. Ahamed²  Md F. Ali²  Z. Tasneem²  S. I. Moyeen²  Faisal R. Badal²  Shahid A. Hasib²  S. Islam²  D. K. Saha²  Md R. Islam²  Ripon K. Chakrabortty³ 

[1] Capability Systems Centre (CSC), University of New South Wales (UNSW), Canberra, ACT, Australia;Department of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh;School of Engineering and Information Technology, University of New South Wales (UNSW), Canberra, ACT, Australia;

关键词: Battery datasets;    battery data repository;    remaining useful life (RUL);    battery management;    li-ion battery;    RUL prediction methods;   

DOI  :  10.1109/ACCESS.2021.3089032

来源: DOAJ

【 摘 要 】

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.

【 授权许可】

Unknown