Lithium-Ion Battery State of Charge Estimation Using Deep Neural Network

Authors

  • Srinivas Singirikonda School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India
  • Yeddula Pedda Obulesu School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

DOI:

https://doi.org/10.13052/dgaej2156-3306.3833

Keywords:

Battery management system, deep learning, feed-forward neural network, lithium-ion battery, state of charge estimation

Abstract

In an electric vehicle (EV), the battery management system (BMS) is crucial for managing the health and safety of the battery. The accurate estimation of battery state of charge (SOC) offers critical information about the battery’s remaining capacity. The SOC of the battery mainly depends on its non-linear internal parameters, battery chemistry, ambient temperature, aging factor etc. So, accurate SOC estimation is still a significant challenge. Many researchers have developed several model-based methods that are more complex to develop. Another approach is a data-driven based SOC estimation algorithm, which is less complex but requires more data and it may be inaccurate. In this context, this paper presents a robust and accurate SOC estimation algorithm for a Lithium-ion battery using a deep learning feed-forward neural network (DLFFNN) approach. The proposed algorithm accurately characterizes the battery’s non-linear behavior. To develop a robust SOC estimation algorithm, data is collected at different temperatures with 5% error in data (4 mV-voltage, 110 mA-current, 5∘C temperature) is added to battery datasets. The obtained results demonstrated that the performance of the proposed DLFNN is robust and accurate on different drive cycles with 1.14% Root mean squared error (RMSE), 0.66% mean absolute error (MAE), and 6.65% maximum error (MAX).

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Author Biographies

Srinivas Singirikonda, School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

Srinivas Singirikonda received his B. Tech degree in Electrical Engineering from Jawaharlal Nehru Technological University, Hyderabad, Telangana, India and M. Tech degree in Control systems from Jawaharlal Nehru Technological University, Hyderabad, Telangana, India and Ph.D. degree from the School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India. His research interest includes Power converters, Electric Vehicles, Battery management systems, AI and ML techniques, EV Fast Charging, EV power train component design and Renewable energy source grid integration.

Yeddula Pedda Obulesu, School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

Yeddula Pedda Obulesu received his B.E degree in Electrical Engineering from Andhra University in 1995 and M.Tech degree in Machine drives and Power Electronics from Indian Institute of Technology, Kharagpur in 1998 and the Ph.D. degree from Jawaharlal Nehru Technological University, Andhra Pradesh, India in 2006. He is currently working as a professor in the School of Electrical Engineering, Vellore institute of technology, Vellore, Tamilnadu, India. His research interest includes Power converters, Electric Vehicles, Micro E-mobility Control of Electrical Drives, EV Fast Charging, Battery Management System, DC Microgrids, DC Distribution, Harmonic Mitigation & Power quality, AI & ML techniques.

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Published

2023-03-03

How to Cite

Singirikonda, S. ., & Obulesu, Y. P. . (2023). Lithium-Ion Battery State of Charge Estimation Using Deep Neural Network. Distributed Generation &Amp; Alternative Energy Journal, 38(03), 761–788. https://doi.org/10.13052/dgaej2156-3306.3833

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Articles