Optimizing vanadium redox flow battery system power loss using particle swarm optimization technique under different operating conditions

Ra, Nawin; Dutta, Ankita; Bhattacharjee, Ankur

doi:10.1002/er.8402

Cited by 5 publications

(4 citation statements)

References 47 publications

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“…Equation (), 34 denotes the terminal voltage of stack,

E_{t} = E_{OCV} + \{n \times (I_{s} \times R_{int})\},

where (

E_{OCV}

) represents the open circuit voltage of the VRFB stack. (

E_{OCV}

) is expressed by Equation (), 34

E_{OCV} = n \times \{E_{Cell_eq} + \frac{2 RT}{F} \ln (\frac{SOC}{1 - SOC}) - I_{d} R_{SD}\},

where “

n

” represents the total count of series cells in the stack.

^{“} {E_{Cell_eq}}^{”}

signifies the equilibrium potential of VRFB cell at 50% SOC.…”

Section: Electrical Equivalent Modelling Of Vrfb Systemmentioning

confidence: 99%

“…“ R SD ” denotes the self‐discharge resistance. VRFB stack power loss

(P_{stack_loss}

) is expressed by,

P_{stack_loss} = I_{s}^{2} . R_{int} .

The VRFB pump power consumption is expressed by Equation (), 34

P_{pump} = \frac{2 (∆ P_{stack} + ∆ P_{pipes}) Q (t)}{η_{pump}},

where,

^{“} ∆ {P_{stack}}^{”}

denotes the drop in the magnitude of pressure inside the stack due to the parallel flow of electrolyte via each cell. During the flow of electrolyte through the horizontal and vertical paths, there is a drop in the magnitude of pressure inside the pipes which connects the tanks and stack.…”

Section: Electrical Equivalent Modelling Of Vrfb Systemmentioning

confidence: 99%

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Prediction of vanadium redox flow battery storage system power loss under different operating conditions: Machine learning based approach

Bhattacharjee

2022

Intl J of Energy Research

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Prediction of battery storage system loss is necessary to further improve the performance reliability and efficiency of the battery storage system. The prediction of the overall system power loss of Vanadium Redox Flow Battery (VRFB) using different machine learning (ML) algorithms has been demonstrated for the first time. Under different operating current levels and electrolyte flow rates, the internal resistance variation and pump power consumption of the practical 1 kW 6 kWh VRFB system dataset have been considered for prediction. The prediction accuracy of ML algorithms has been analysed in detail based on the regression metrics such as correlation coefficient (R 2 ), mean absolute error (MAE) and root mean square error (RMSE). It is observed that Ensemble learning (EL) based Adaptive Boost (AdaBoost) algorithm is superior in predicting VRFB system loss compared to that of linear regression (LR), support vector regression (SVR) algorithms. The AdaBoost algorithm statistically presents the best prediction results with prediction error parameters obtained as R 2 , MAE and RMSE of around 0.99, 3.242 and 4.073, respectively, for 50 A stack current profile with a flow rate of 18 L/min. The prediction results obtained in this paper claim to be beneficial for designing optimized interfacing of VRFB storage with renewable energy sources and other power system applications.

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“…Equation (), 34 denotes the terminal voltage of stack,

E_{t} = E_{OCV} + \{n \times (I_{s} \times R_{int})\},

where (

E_{OCV}

) represents the open circuit voltage of the VRFB stack. (

E_{OCV}

) is expressed by Equation (), 34

E_{OCV} = n \times \{E_{Cell_eq} + \frac{2 RT}{F} \ln (\frac{SOC}{1 - SOC}) - I_{d} R_{SD}\},

where “

n

” represents the total count of series cells in the stack.

^{“} {E_{Cell_eq}}^{”}

signifies the equilibrium potential of VRFB cell at 50% SOC.…”

Section: Electrical Equivalent Modelling Of Vrfb Systemmentioning

confidence: 99%

“…“ R SD ” denotes the self‐discharge resistance. VRFB stack power loss

(P_{stack_loss}

) is expressed by,

P_{stack_loss} = I_{s}^{2} . R_{int} .

The VRFB pump power consumption is expressed by Equation (), 34

P_{pump} = \frac{2 (∆ P_{stack} + ∆ P_{pipes}) Q (t)}{η_{pump}},

where,

^{“} ∆ {P_{stack}}^{”}

Section: Electrical Equivalent Modelling Of Vrfb Systemmentioning

confidence: 99%

Prediction of vanadium redox flow battery storage system power loss under different operating conditions: Machine learning based approach

Bhattacharjee

2022

Intl J of Energy Research

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“…Ra et al developed a power loss optimization method for the battery storage system based on PSO technology. This method maximized the efficiency of the overall battery system (Ra et al 2022). Sevilgen et al used PSO to optimize the liquid cooling system of battery modules.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive testing technology for new energy vehicle power batteries based on improved particle swarm optimization

Liu,

Liang

2024

Energy Inform

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As the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm optimization, and SVR is constructed. It optimizes regression parameters through the introduced optimization strategy. Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The model predicted a mean square error of 0.0011 for battery 5, 0.0007 for battery 6, 0.0022 for battery 7, and 0.0013 for battery 18. In the prediction of different battery types, the mean square error of the NIMH battery was reduced by 0.0008 compared with the particle swarm optimization-support vector regression algorithm, and by 0.0005 compared with the unscented particle filtering-regression vector regression algorithm. The mean square error of lithium-iron phosphate battery was reduced by 0.0008 and 0.0004 respectively compared with comparison models. The mean square error value of lithium titanate battery was reduced by 0.0007 and 0.0003 respectively in the research model compared with comparison models. It improves the prediction accuracy in lithium-ion batteries. Its application in battery health management can provide important technical support for improving battery performance and extending service cycles. The proposed method can be used for battery monitoring and management of power grid energy storage system. By accurately predicting the capacity decline of battery, the operation strategy of energy storage system can be optimized to ensure the efficient operation and long life of the system. The battery management system can be used for drones and aviation equipment to predict battery health and capacity decline in real time, ensuring the safety and reliability of flight missions.

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“…13 In recent years, a combination of numerical simulation and experimental methods has been used to study the capacity of VRFB under different operating conditions, leading to significant achievements in this field. 14 The effects of operating conditions on battery performance were investigated by Perales et al 15 including polarization phenomena, hydrogen evolution reactions, crossover, and self-discharge. Guo et al 16 proposed innovative flow field structures to enhance mass transfer and mitigate polarization, thereby enhancing efficiency and mitigating capacity degradation.…”

mentioning

confidence: 99%

Effect of Operating Conditions on the Capacity of Vanadium Redox Flow Batteries

Ma,

Huang,

et al. 2024

J. Electrochem. Soc.

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Vanadium redox flow batteries (VRFBs) present a viable solution to address the intermittent power output challenge associated with wind and solar energy generation. However, their development is impeded by their low energy density and high cost. To achieve the objective of cost reduction, it is crucial to optimize operating conditions, minimize capacity loss, and enhance battery performance. Through meticulous experimental analysis, this study thoroughly examines the impact of membrane thickness, current density, flow rate, and self-discharge on battery capacity. The experimental findings reveal that an increase in membrane thickness results in elevated resistance to proton transport, thereby weakening electrochemical reactions. Moreover, surpassing critical values for current density and flow rate also leads to a decrease in capacity. Prolonged shelving induces severe self-discharge reactions that accelerate deterioration of capacity fade. This research suggests that obtaining optimal operational parameters can effectively mitigate battery capacity fade.

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Optimizing vanadium redox flow battery system power loss using particle swarm optimization technique under different operating conditions

Cited by 5 publications

References 47 publications

Prediction of vanadium redox flow battery storage system power loss under different operating conditions: Machine learning based approach

Prediction of vanadium redox flow battery storage system power loss under different operating conditions: Machine learning based approach

Comprehensive testing technology for new energy vehicle power batteries based on improved particle swarm optimization

Effect of Operating Conditions on the Capacity of Vanadium Redox Flow Batteries

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