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

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 dramatical...

Full description

Saved in:
Bibliographic Details
Published in2023 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
LanguageEnglish
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
Online AccessGet full text

Cover

More Information
Summary: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.
DOI:10.1109/CSITSS60515.2023.10334095