Advanced Intelligent approach for state of charge estimation of lithium-ion battery

ABSTRACT

The commercialization of lithium-ion batteries (LIBs) is rapidly increasing due to a variety of inherent and extrinsic parameters. The State of Charge (SOC), which denotes the amount of remaining capacity, is one of the most important performance metrics for these batteries. As a result, achieving a reliable and precise SOC estimation is essential for the greatest durability and security of LIBs. Estimating the SOC is important to improve the performance and robust utilization of LIBs. Here, this paper uses artificial neural network-based machine learning and deep learning approaches to estimate the battery state of charge. The battery voltage, current, and temperatures have been precisely integrated as input for the models. The proposed model’s accuracy, reliability, and robustness are evaluated using available datasets. The mean absolute error was found to be in the range of 0.0030 to 0.0035, and root mean square errors 0.0043 to 0.0047 were obtained at 0 and 10°C operating temperatures. The outcomes demonstrate that the models can successfully estimate the SOC under different temperature conditions.

KEYWORDS:

• Electric Vehicle
• lithium-ion battery
• state of charge
• convolutional neural network
• artificial intelligence

Disclosure statement

No potential conflict of interest was reported by the authors.

Dataset availability statement

Dataset analysis and forecasting have been performed utilizing publicly available data from Mendeley Data.

Declaration

The authors declare that the present research has no financial or personal ties to any individual or group that could unreasonably affect this work. This manuscript is the author’s work and does not contain material from other sources. All data measures are accurate and unaltered results, and this work has never been published or submitted to another journal for publication consideration. Also, none of the authors has any financial or scientific conflicts of interest concerning the research described in this manuscript.

Additional information

Funding

No specific grant has been given to this research by funding agencies/organizations.

Notes on contributors

Deepak Kumar

Deepak Kumar received his B.Tech. degree in Electrical and Electronics Engineering from Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh, Lucknow, India, in 2017, and M.Tech. degree in Nanoscience and Technology from the Department of Applied Physics at Delhi Technological University, Delhi, India, in 2021. He is currently pursuing PhD degree at Delhi Technological University, Delhi, India with the Department of Electrical Engineering. He has published two research articles in reputed international/national journals. His research interests include battery technology, energy storage in electric vehicles, and AI-based battery management ystems/battery state estimation.

M. Rizwan

Dr. M. Rizwan did his post-doctoral research at Virginia Polytechnic Institute and State University, USA. He is presently working as a Professor at the Department of Electrical Engineering, Delhi Technological University, Delhi, India. He has almost 20 years of teaching and research experience. Dr. Rizwan has successfully completed three research projects in the area of renewable energy systems and published and presented more than 175 research papers in reputed international/national journals including IEEE transactions and conference proceedings. Dr. Rizwan has published one book titled Grid Integration of Solar Photovoltaic Systems, CRC Press, (Taylor and Francis Group), 2017, and Edited one book for AIP Publishing, USA. He is the recipient of Raman Fellowships for Post-Doctoral Research for Indian Scholars in USA, DST Start up Grants (Young Scientists) and many more. His area of interest includes soft computing applications in power engineering, renewable energy systems, building energy management, smart grid etc. He is a Sr. Member of IEEE, Life Member of ISTE, Life Member of SSI, Member of International Association of Engineers (IAENG), and many other reputed societies.

Amrish K. Panwar

Dr. Amrish K Panwar completed his MS degree from Chaudhary Charan Singh (CCS) University, India, and his PhD degree from Indian Institute of Technology (IIT) Roorkee, India in 2005. After completing PhD degree, he has worked as a post-doctoral research associate at the Department of Metallurgical and Materials Engineering, IIT Kharagpur, India. Before joining Delhi Technological University (DTU), he has also worked as senior lecturer in Jaypee University of Engineering and Technology (JUET), India. Presently, he is working as Assistant Professor in the Department of Applied Physics, Delhi Technological University, New Delhi, India. His area of research includes: energy storage and conversion devices (Li+ /Na+ batteries/supercapacitors/solar cells), surface modification, wetting adhesion, and nanocomposites.