An imperative role of studying existing battery datasets and algorithms for battery management system

Gopal  Krishna; Rajesh  Singh; Anita  Gehlot; Pardeep  Singh; Sandeep  Rana; Shaik Vaseem  Akram; Kapil  Joshi

doi:10.18488/76.v10i2.3413

Authors

Gopal Krishna Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, Uttarakhand, India. https://orcid.org/0000-0003-1097-8347
Rajesh Singh Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, Uttarakhand, India, and Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, CP, Mexico. https://orcid.org/0000-0002-3164-8905
Anita Gehlot Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, Uttarakhand, India, and Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, CP, Mexico. https://orcid.org/0000-0001-6463-9581
Pardeep Singh Graphic Era Hill University, Dehradun-248002, Uttarakhand, India. https://orcid.org/0000-0002-0368-4757
Sandeep Rana G B Pant Institute of Engineering and Technology, Pauri Garhwal-246194 Uttarakhand, India. https://orcid.org/0009-0007-4140-6161
Shaik Vaseem Akram Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, Uttarakhand, India. https://orcid.org/0000-0001-8438-4387
Kapil Joshi Uttaranchal Institute of Technology, Uttaranchal University, Dehradun-248007, Uttarakhand, India. https://orcid.org/0000-0003-1097-8347

DOI:

https://doi.org/10.18488/76.v10i2.3413

Abstract

Numerous portable technologies, including electric vehicles, cell phones, and laptops, are powered by batteries. The use of batteries is increasing due to the widespread usage of battery energy storage in the generation of renewable energy. This has also resulted in an increase in the number of negative incidents related to batteries and had a significant negative economic impact on industries. The shortcomings in the traditional monitoring of lithium-ion batteries have been overcome through the implementation of advanced technologies. However, few studies have discussed the use of distinct datasets in the implementation of an intelligent battery management system (BMS). This paper presents a discussion on the choice of dataset variables and applied algorithms for the implementation of effective BMS with artificial intelligence (AI) and machine learning (ML). The study analyzed the use of different datasets, including the National Aeronautics and Space Administration (NASA) battery dataset, to improve BMS. It found that the dataset variables must include the terminal voltage, terminal current, charge current, charge voltage, internal resistance, temperature, and cycle to calculate the state of health (SoH). In future, BMS hardware will be developed to obtain more precise results using AI and ML-based prediction models, utilizing the selected dataset characteristics and variables to achieve longer battery life.