Dynamic Electrochemical Model For SOFC-Stacks

AbstractBattery performance is strongly correlated with electrode microstructure. Electrode materials for lithium-ion batteries have complex microstructure geometries that require millions of degrees of freedom to solve the electrochemical system at the microstructure scale. A fast-iterative solver with an appropriate preconditioner is then required to simulate large representative volume in a reasonable time. In this work, a finite element electrochemical model is developed to resolve the concentration and potential within the electrode active materials and the electrolyte domains at the microstructure scale, with an emphasis on numerical stability and scaling performances. The block Gauss-Seidel (BGS) numerical method is implemented because the system of equations within the electrodes is coupled only through the nonlinear Butler–Volmer equation, which governs the electrochemical reaction at the interface between the domains. The best solution strategy found in this work consists of splitting the system into two blocks—one for the concentration and one for the potential field—and then performing block generalized minimal residual preconditioned with algebraic multigrid, using the FEniCS and the Portable, Extensible Toolkit for Scientific Computation libraries. Significant improvements in terms of time to solution (six times faster) and memory usage (halving) are achieved compared with the MUltifrontal Massively Parallel sparse direct Solver. Additionally, BGS experiences decent strong parallel scaling within the electrode domains. Last, the system of equations is modified to specifically address numerical instability induced by electrolyte depletion, which is particularly valuable for simulating fast-charge scenarios relevant for automotive application.

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Development of catalytic combustion and CO2 capture and conversion technology

International Journal of Coal Science & Technology ◽

10.1007/s40789-021-00444-2 ◽

2021 ◽

Author(s):

Zhibin Yang ◽

Ze Lei ◽

Ben Ge ◽

Xingyu Xiong ◽

Yiqian Jin ◽

...

Keyword(s):

Power Generation ◽

Fuel Cell ◽

Catalytic Combustion ◽

Coal Gasification ◽

Solid Oxide ◽

Multi Scale ◽

Electrolysis Cells ◽

Integrated Gasification ◽

Solid Oxide Electrolysis Cells ◽

Sofc Stacks

AbstractChanges are needed to improve the efficiency and lower the CO2 emissions of traditional coal-fired power generation, which is the main source of global CO2 emissions. The integrated gasification fuel cell (IGFC) process, which combines coal gasification and high-temperature fuel cells, was proposed in 2017 to improve the efficiency of coal-based power generation and reduce CO2 emissions. Supported by the National Key R&D Program of China, the IGFC for near-zero CO2 emissions program was enacted with the goal of achieving near-zero CO2 emissions based on (1) catalytic combustion of the flue gas from solid oxide fuel cell (SOFC) stacks and (2) CO2 conversion using solid oxide electrolysis cells (SOECs). In this work, we investigated a kW-level catalytic combustion burner and SOEC stack, evaluated the electrochemical performance of the SOEC stack in H2O electrolysis and H2O/CO2 co-electrolysis, and established a multi-scale and multi-physical coupling simulation model of SOFCs and SOECs. The process developed in this work paves the way for the demonstration and deployment of IGFC technology in the future.

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A Nonlinear Observer SOC Estimation Method Based on Electrochemical Model for Lithium-Ion Battery

IEEE Transactions on Industry Applications ◽

10.1109/tia.2020.3040140 ◽

2021 ◽

Vol 57 (1) ◽

pp. 1094-1104

Author(s):

Yuntian Liu ◽

Rui Ma ◽

Shengzhao Pang ◽

Liangcai Xu ◽

Dongdong Zhao ◽

...

Keyword(s):

Lithium Ion Battery ◽

Estimation Method ◽

Lithium Ion ◽

Nonlinear Observer ◽

Soc Estimation ◽

Electrochemical Model

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Global Sensitivity Analysis on Temperature-Dependent Parameters of A Reduced-Order Electrochemical Model And Robust State-of-Charge Estimation at Different Temperatures

Energy ◽

10.1016/j.energy.2021.120024 ◽

2021 ◽

Vol 223 ◽

pp. 120024

Author(s):

Guodong Fan ◽

Xiaoyu Li ◽

Ruigang Zhang

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

State Of Charge ◽

Temperature Dependent ◽

Reduced Order ◽

Global Sensitivity ◽

Different Temperatures ◽

Electrochemical Model ◽

State Of Charge Estimation

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Electrochemical Model-Based Investigation of Thick LiFePO4 Electrode Design Parameters

Modelling ◽

10.3390/modelling2020014 ◽

2021 ◽

Vol 2 (2) ◽

pp. 259-287

Author(s):

Robert Franke-Lang ◽

Julia Kowal

Keyword(s):

Lithium Ion Batteries ◽

Porous Electrode ◽

Lithium Ion ◽

Size Composition ◽

Design Parameters ◽

Model Parameters ◽

Electrode Structure ◽

Transport Channel ◽

Scale Particle ◽

Electrochemical Model

The electrification of the powertrain requires enhanced performance of lithium-ion batteries, mainly in terms of energy and power density. They can be improved by optimising the positive electrode, i.e., by changing their size, composition or morphology. Thick electrodes increase the gravimetric energy density but generally have an inefficient performance. This work presents a 2D modelling approach for better understanding the design parameters of a thick LiFePO4 electrode based on the P2D model and discusses it with common literature values. With a superior macrostructure providing a vertical transport channel for lithium ions, a simple approach could be developed to find the best electrode structure in terms of macro- and microstructure for currents up to 4C. The thicker the electrode, the more important are the direct and valid transport paths within the entire porous electrode structure. On a smaller scale, particle size, binder content, porosity and tortuosity were identified as very impactful parameters, and they can all be attributed to the microstructure. Both in modelling and electrode optimisation of lithium-ion batteries, knowledge of the real microstructure is essential as the cross-validation of a cellular and lamellar freeze-casted electrode has shown. A procedure was presented that uses the parametric study when few model parameters are known.

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