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Research Papers

A Constrained Extended Kalman Filter for State-of-Charge Estimation of a Vanadium Redox Flow Battery With Crossover Effects

[+] Author and Article Information
Victor Yu

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: victory118@utexas.edu

Alex Headley

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: ajhead2@gmail.com

Dongmei Chen

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: dmchen@me.utexas.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 29, 2013; final manuscript received February 2, 2014; published online April 8, 2014. Assoc. Editor: Evangelos Papadopoulos.

J. Dyn. Sys., Meas., Control 136(4), 041013 (Apr 08, 2014) (7 pages) Paper No: DS-13-1217; doi: 10.1115/1.4026654 History: Received May 29, 2013; Revised February 02, 2014

One of the main issues with vanadium redox flow batteries (VRFBs) is that vanadium ions travel across the membrane during operation which leads to a concentration imbalance and capacity loss after long-term cycling. Precise state-of-charge (SOC) monitoring allows the operator to effectively schedule electrolyte rebalancing and devise a control strategy to keep the battery running under optimal conditions. However, current SOC monitoring methods are too expensive and impractical to implement on commercial VRFB systems. Furthermore, physical models alone are neither reliable nor accurate enough to predict long-term capacity loss due to crossover. In this paper, we present an application of using an extended Kalman filter (EKF) to estimate the total vanadium concentration in each half-cell by combining three voltage measurements and a state prediction model without crossover effects. Simulation results show that the EKF can accurately predict capacity loss for different crossover patterns over a few hundred cycles.

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Figures

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Fig. 1

Schematic of a VRFB system

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Fig. 2

Measuring the OCV at the positive and negative tanks separately using two reference cells

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Fig. 7

Actual and estimated concentration ratios over 200 charge/discharge cycles using a Selemion AMV membrane with η = 50. Each data point corresponds to the beginning of a charge/discharge cycle. Top: First 25 cycles. Bottom: Cycles 26–200.

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Fig. 4

Actual and estimated concentration ratios over 200 charge/discharge cycles using a Nafion 115 membrane with η = 50. Each data point corresponds to the beginning of a charge/discharge cycle. Top: First 25 cycles. Bottom: Cycles 26–200.

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Fig. 5

Actual and estimated concentration ratios over 200 charge/discharge cycles using a Nafion 115 membrane with η = 200. Each data point corresponds to the beginning of a charge/discharge cycle. Top: First 25 cycles. Bottom: Cycles 26–200.

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Fig. 6

Actual and estimated concentration ratios over 200 charge/discharge cycles using a Selemion CMV membrane with η = 50. Each data point corresponds to the beginning of a charge/discharge cycle. Top: First 25 cycles. Bottom: Cycles 26–200.

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Fig. 3

Actual and estimated concentration ratios over 200 charge/discharge cycles using a Nafion 115 membrane with η = 1. Each data point corresponds to the beginning of a charge/discharge cycle. Top: First 25 cycles. Bottom: Cycles 26–200.

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