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

Shahid A Hasib,Faisal R Badal,Michael J Ryan,S Islam,Md F Ali,Z Tasneem,Md R Islam,D K Saha,Md H Ahamed,Ripon K Chakrabortty,Sajal K Das,S I Moyeen

doi:10.1109/access.2021.3089032

Shahid A Hasib, Faisal R Badal + Show 10 more

Open Access

https://doi.org/10.1109/access.2021.3089032

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Abstract

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.

Highlights

E Nergy storage has become one of the predominant sectors as most of the consumer electronics are powered with battery-like technologies and electricity generation is rapidly growing from renewable energy sources [1]
FOR FUTURE WORK The main goal of this review is to review various battery remaining useful life (RUL) prediction approaches and battery management systems, and to provide sufficient information about commercially or publicly available battery data sets
The battery is the power source for many consumer electronics, mobile phones, laptop computers, electric vehicles, spacesuits, submarines, rovers, and other devices that require with stored power

Summary

Introduction

E Nergy storage has become one of the predominant sectors as most of the consumer electronics are powered with battery-like technologies and electricity generation is rapidly growing from renewable energy sources [1]. 4) Thermal storage for high efficiency and long lifetimes, such as storage of thermochemical energy, storage of sensible heat, and storage of latent heat [3] Among this rechargeable electrochemical storage or bat-. The data set provides a general and realistic use of electrochemical cells to examine and corroborate standard models and the associated system recognition operation. To generate the Training Set and the Test Set, two different trips were considered Both trips were compiled with a mixture of highway, extra-urban and urban driving cycles to find a realistic environment with readjusting and battery charging phases. Some other applications of these data sets include prognostic approaches, RUL estimation, health monitoring of aeronautical batteries, and much other research in [56]– [64]. All publicly and commercially available data-sets are compiled and summarised in Table 2 for a better understanding

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