The lithium iron phosphate (LFP) battery has more nonlinear characteristic than other battery type. For this reason, when we use electrical equivalent circuit model and the extended Kalman filter (EKF) for estimating the SOC, the estimation performance can be decreased in the nonlinear region. This paper proposes an advance estimation method of state of charge (SOC) for lithium iron phosphate (LFP) batteries. To improve the model accuracy, this paper utilizes the nonlinear observer for identifying the internal parameters of batteries. Furthermore, to reduce the nonlinear effect of the LFP batteries, this paper recast the Kalman process. Therefore, through the proposed method, the performance of SOC estimation can be more accurate and the computational burden is decreased when we apply the embedded system.
An equivalent circuit model for Vanadium Redox Batteries via hybrid extended Kalman filter and Particle filter methods
A B S T R A C T
This paper proposes a model for parameter estimation of Vanadium Redox Flow Battery based on both the electrochemical model and the Equivalent Circuit Model. The equivalent circuit elements are found by a newly proposed optimization to minimized the error between the Thevenin and KVL-based impedance of the equivalent circuit. In contrast to most previously proposed circuit models, which are only introduced for constant current charging, the proposed method is applicable for all charging procedures, i.e., constant current, constant voltage, and constant current-constant voltage charging procedures. The proposed model is verified on a nine-cell VRFB stack by a sample constant current-constant voltage charging. As observed, in constant current charging mode, the terminal voltage model matches the measured data closely with low deviation; however, the terminal voltage model shows discrepancies with the measured data of VRFB in constant voltage charging. To improve the proposed circuit model’s discrepancies in constant voltage mode, two Kalman filters, i.e., hybrid extended Kalman filter and particle filter estimation algorithms, are used in this study. The results show the accuracy of the proposed equivalent with an average deviation of 0.88% for terminal voltage model estimation by the extended KF-based method and more »
- Editors:
- Uwe Sauer, Dirk
- Award ID(s):
- 2039564
- Publication Date:
- NSF-PAR ID:
- 10229152
- Journal Name:
- Journal of energy storage
- ISSN:
- 2352-152X
- Sponsoring Org:
- National Science Foundation
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Abstract— Typically, the electrochemical model and Equivalent Circuit Model-based (ECM) algorithms of Vanadium Redox Flow Batteries (VRFB) are complex and require high computation-time, thus not suitable to be used in the Battery Management Systems (BMS). Therefore, two simplified fast ECM-based estimation algorithms are proposed for the VRFB’s State of Charge (SoC) estimation. The methods are proposed based on two different parameter identification algorithms, namely discharge pulse response and the optimization-based parameter identification for the first-order ECM. The proposed approaches are further extended by an innovative, simplified mathematical model for the capacity fade of VRFBs based on the battery's electrochemical model. The simplified capacity loss model facilitates non-complex and fast estimation of VRFB’s State of Health (SoH), useful for modeling in the BMS. This has been led to a more accurate SoC estimation in the long-term use of the battery when the VRFB’s capacity fades due to electrolyte volume loss. Although the proposed joint estimation of VRFB’s SoC and SoH estimations are simpler to be modeled in the BMS, the proposed estimations are still accurate since the models consider enough electrochemical details of VRFBs. The accuracy, less complexity, reduced computation-time, and lower BMS memory storage highlight the proposed algorithms. Keywords—Battery Managementmore »
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Uwe Sauer, Dirk (Ed.)ABSTRACT State of Charge (SoC) and discharge capacity of the batteries are parameters that cannot be determined directly from the battery monitoring and control system and requires estimation. Current and voltage sensors have inherent error and delay leading to inaccurate measurements leading to inaccurate SoC and discharge capacity estimations. These sensors also have an additional cost to the battery system. This paper proposes a sensorless approach to estimate parameters of Vanadium Redox Flow Batteries (VRFBs) for both CC and CV charging methods by estimating battery current in CV mode and terminal voltage in CC mode. The results of estimations by the sensorless approach show a maximum relative error of 0.0035 in estimating terminal voltage in CC charging and a maximum relative error of 0.045 in estimating charging current in CV mode. Furthermore, long- term operation of vanadium redox flow batteries causes ion diffusions across the membrane and the depletion of active materials, which leads to capacity fading in VRFBs and inaccurate SoC estimation. To address the inaccuracy of SoC estimation in the long-term use of the battery, the capacity fading model is also considered for VRFBs in this paper. Experimental results show a 19% electrolyte volume change in the positivemore »
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