A Data-Driven Comparative Analysis of Lithium-Ion Battery State of Health and Capacity Estimation

Abstract

Researchers are focused toward the development of electric vehicles and hybrid electric vehicles for the preservation of the ecosystem. Energy storage, conversely, became essential with these changes in vehicular technology. This opens new research fronts and introduces complex problems that are to be investigated regarding the primary storage element. Battery modeling offers improved efficiency, safety, and reliability. Lithium-ion batteries are a popular source of energy in many applications, especially for electric vehicles because of their high performance and high energy density. However, there exist some inherent issues like complex electrochemical reactions that make accurate monitoring a challenging task. This article performs a comparative study of the existing battery state of health (SOH) monitoring and capacity estimation techniques using the NASA dataset for lithium-ion batteries. Charging cycle datasets for voltage, current, and temperature are used for feature identification and extraction. After the modeling of extracted features, the SOH and the capacity of four batteries are estimated using three data-driven techniques. Including convolutional neural network, feed-forward neural network, and long short-term memory (LSTM), and their results are compared. The comparative analysis highlights an outstanding performance and high accuracy of the LSTM-based machine learning technique because of the inherited long-term memory of the LSTM. The study, therefore, recommends the use of LSTM to researchers for battery health monitoring and capacity estimation with the highest possible accuracy.

Keywords:

• capacity estimation
• convolutional neural network
• feed-forward neural network
• long short-term memory
• state of health

ACKNOWLEDGMENT

The authors thank NASA Ames prognostics for providing an open source battery data repository for advancing this domain knowledge.

Additional information

Notes on contributors

Shehzar Shahzad Sheikh

Shehzar Shahzad Sheikh (Member, IEEE) was born in Quetta, Pakistan. He received B.S. in Electrical power engineering with distinction in electrical engineering from Balochistan University of Information Technology, Engineering & Management Sciences (BUITEMS), Quetta in 2016 and M.S. in Electrical Power Engineering from National University of Sciences and Technology (NUST) Islamabad-Pakistan in 2019. He has published several articles in IEEE, MDPI and IOP journals. He has been serving as a technical committee member for IEEE Access, Elsevier energy journals and international IEEE conferences. His research interest includes energy storage for electrical vehicles and electric aircrafts.

Fawad Ali Shah

Fawad Ali Shah (Student Member IEEE) graduated from Balochistan University of IT, Engineering and Management Sciences in Electronic Engineering. Currently he is currently pursuing his M.S. in Lasers and Photonics from Ruhr University Germany. His research includes laser’s technologies and energy storage.

Syed Owais Athar

Syed Owais Athar (Member, IEEE) was born in Quetta, Pakistan. He received B.S. in electronic engineering and M.S. with distinction in electrical engineering from Balochistan University of Information Technology, Engineering & Management Sciences (BUITEMS), Quetta, Pakistan in 2012 and 2018, respectively. He is currently enrolled as a Fulbright Ph.D. Scholar at College of Electrical and Computer Engineering, University of Nebraska Lincoln, NE, USA. His research interests include electric power components and systems.

Hassan Abdullah Khalid

Hassan Abdullah Khalid received the B.Sc. degree in electrical engineering from Air University, Islamabad, Pakistan, in 2007, the M.Sc. degree in electrical power engineering from the Chalmers University of Technology, Gothenburg, Sweden, in 2010, and the Ph.D. degree from the University of L'Aquila, L'Aquila, Italy, in 2016. Since 2016, he has been with the National University of Science and Technology, Islamabad, where he is currently an Associate Professor. His current research interests include systems control with applications in power electronics, energy conversion, renewable energy, and smart grids.