"State-of-Charge Estimation Methodology for Lithium-Ion Batteries Utilizing Extended Kalman Filtering"

• Published in: 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) pp. 1 - 5
• Main Authors: Vidya, Raja, M, Uttara Kumari, R, Sridhar, S R, Dhanush, Sharma, Shriram J, H S, Ullas Vishwakarma, G, Prajwal B
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.11.2023
• Subjects: Algorithmic improvements; Battery charge measurement; Battery management systems; Estimation; Extended Kalman Filtering (EKF); Lithium-ion batteries; Measurement uncertainty; State-of-Charge Estimation (SOC) and Portable Electronics; Technological innovation; Temperature measurement
• DOI: 10.1109/CSITSS60515.2023.10334095

This paper describes a novel extended Kalman filter (EKF) technique for accurate state-of-charge (SoC) estimation in lithium-ion batteries, which is defined using the second-order Thevenin model and confirmed through pulse discharge testing. The suggested innovations have the potential to dramatically enhance battery management in a variety of applications, including portable electronics, renewable energy systems, and electric vehicles. The approach achieves amazing precision in SoC estimate, with an error rate of less than 1 %, answering the rigorous requirements of battery management systems and contributing to the effective consumption and management of lithium-ion battery resources.