Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications

For taking the advantages of battery in the energy storage, advanced methods are required to accurately monitor and control the battery via the battery management system (BMS). This study investigates a more efficient method to increase accuracy and robustness without the high magnitude of the trans...

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Bibliographic Details
Published in:Energy (Oxford) 2021-10, Vol.232, p.121023, Article 121023
Main Authors: Park, Jinhyeong, Kim, Kunwoo, Park, Seongyun, Baek, Jongbok, Kim, Jonghoon
Format: Article
Language:English
Subjects:
Accuracy
Adaptability
Archives & records
Batteries
Discrete variational derivative
Dual extended kalman filter
Electric power
Energy storage
Equivalent circuit model
Extended Kalman filter
Historical account
Identification methods
Linear functions
Lithium
Lithium-ion batteries
Local linearization
Online parameter identification
Open circuit voltage
Parameter identification
Polynomials
Power management
Rechargeable batteries
Robustness
SOC/Capacity estimator
State of charge
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Summary:For taking the advantages of battery in the energy storage, advanced methods are required to accurately monitor and control the battery via the battery management system (BMS). This study investigates a more efficient method to increase accuracy and robustness without the high magnitude of the transmitted data. An alternative approach to overcome those problems, the dual extended Kalman filter (DEKF) can be selected because it can archive the good accuracy and robustness using less historical data. A major focus in DEKF is how to reflect state-of-charge (SOC) - open-circuit voltage (OCV) relation, which is the crucial characteristics of the battery. Most of the prior research has applied the SOC-OCV relation using a non-linear function such as a polynomial equation. However, since the nonlinear function is defined by the experimental data in the conventional method, the BMS needs larger storage for estimating the SOC and state-of-health (SOH). To overcome the limitation of the conventional DEKF, this study proposes improved DEKF combined with a discrete derivative method based on parameter identification. Thus, the main objective of this study is to construct an efficient and simple SOC-OCV function using the discretization method and online parameter identification method. Experimental studies using two different types of batteries sets illustrate the high accuracy and adaptability of the proposed framework in lithium-ion battery SOC and SOH estimation. •Complementary approach to reflect the aging factor in SOC and capacity estimator is proposed.•Local linearization method is proposed for dual extended Kalman filter.•SOC-OCV relationship is updated to dual extended Kalman filter in real-time.•The storage space of battery management system can be reduced using proposed method.•Performance of the proposed method is verified by two type batteries and comparison results.
ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2021.121023