A new hybrid filter-based online condition monitoring for lithium-ion batteries

This paper proposes a novel online condition monitoring algorithm estimating battery states and model parameters. The proposed method includes: 1) an electrical circuit battery model incorporating the hysteresis effect, 2) an extended Kalman Filter-based online parameter identification algorithm for...

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Bibliographic Details
Main Authors: Kim, Taesic, Adhikaree, Amit, Daewook Kang, Myoungho Kim, Juwon Baek
Format: Conference Proceeding
Language:English
Subjects:
Batteries
Battery condition monitoring
battery model
Condition monitoring
dual extended Kalman filter (DEKF)
Estimation
Filtering algorithms
Hysteresis
Integrated circuit modeling
smooth variable structure filter (SVSF)
State of charge
state of charge (SOC)
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Summary:This paper proposes a novel online condition monitoring algorithm estimating battery states and model parameters. The proposed method includes: 1) an electrical circuit battery model incorporating the hysteresis effect, 2) an extended Kalman Filter-based online parameter identification algorithm for the electrical battery model, and 3) a smooth variable structure filter (SVSF)-based state estimation algorithm for state of charge (SOC) estimation. The proposed method enables an accurate and robust condition monitoring for lithium-ion batteries. Since the proposed hybrid filter further reduces the complexity compared to existing dual extended Kalman filter (DEKF), it is much more suitable for the real-time embedded battery management system (BMS) application. Simulation studies validate the effectiveness of the proposed strategy.
ISSN:2154-0373
DOI:10.1109/EIT.2017.8053324