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EIP - GENIE ELECTROCHIMIQUE
EIP - GENIE ELECTROCHIMIQUE
EIP - GENIE ELECTROCHIMIQUE

> Equipes > EIP > GENIE ELECTROCHIMIQUE (EIP)

Modelling and Electrochemical engineering (EIP)*

Battery

Batteries were deeply studied in the EIP research group such as an electrochemical system. Our investigations concern mainly SOC determination, fast charging and durability

The state of charge of Li-battery can also be determined from measurement of the voltage at non-zero current. The precision this determination depends on slope of the curve characteristic of discharging of the battery according to its state of charge as a case in point, e calibration curve can be obtained in like manner for determining the state of charge. A “good” calibration curve employ a weak number of “true” parameters in order to make it useful.


Figure 1. Experimental and fitted curves of discharging battery, test bench for battery ageing

 

This technique is also useful for fast charging battery technic; thanks to this kind of method, the potential control is more efficient.  

 

Fuel cells

 Durability of PEMFC system is “the worry” of EIP group, and then to develop new strategy to detect the degradation is a continuous endeavor.

Many phenomena such as MEA degradation, membrane drying and electrode flooding affect the current density distribution. For example, the measurement of magnetic fields allows for the building of a three dimensional picture of the internal current density possible. The magnetotomography can supply a lot of information about the operating conditions within a fuel cell. It is possible to estimate an averaged 2D current density identification by solving an inverse problem, based on the relationship between the currents and a magnetic field. Therefore to solve an inverse problem, we need a “good” direct model”. This PEMFC Stack models require the consideration of interaction between cells taking into account that single cells have different locations and cannot be identical from a material properties or operational conditions point of view. This model of the stack is performed by solving the steady state charge conservation equation (local scale and stack scale).

 

Figure 2. Simulated current distributions (%) through a PEM Stack, experimental apparatus of magnetic field measurements

 

The model of stack coupled to magnetic sensor highlights the nonhomogeneity of current density through the cell and between the cells of the stack.

 

  

Electrolyzer

The Electrochemical engineering research group (IEP sub group) proposes thanks to the modelling to improve the operation of industrial electrochemical reactors

 The efficiency of industrial electrochemical processes strongly depends on mass transfer. For example, electrochemical reactions can be enhanced by pumped electrolyte flow, which increases mass transfer at the electrode surface. Alternatively, a flow can be induced by the electrochemical bubble production. Furthermore in alkaline water electrolysis process the additional resistance arising from partial coverage of the electrodes by the bubbles was critical in energy efficiency. A better understanding of bubble behavior would provide a scientific guidance to minimize this resistance and contribute to the development of hydrogen production. The developed approach in EIP group shows for Prx >1, that it is possible to predict the evolution of the bubble plumes along the electrodes in free and forced convection. It is observed that the most influent parameter is the bubble diameter, which depends on various parameters (e.g. electrolyte viscosity, pressure, current density).  


Figure 3. Experimentally measured velocity profiles versus simulation results and test bench of alkali electrolysis (membrane less) cell

Thanks to this results it is possible to optimize the operation of electrolyze using the analysis of classical dimensionless numbers such as Reynolds (forced) or Rayleigh (free) “like” number and equivalent Prandtl number.

 

Modeling for electroanalytical



It is well know that electrochemical technics are powerful tools for investigation of physical phenomena using electroanalytical technics. The EIP skills of Electrochemical modelling are employed in various electroanalytical methods

 For example in SECM, the most widespread operating mode is based on the evaluation of the substrate regeneration rate of a redox mediator present in solution and consumed at the microelectrode. This situation, generally called the feedback mode.  Therefore, a model implementing the surface conductivity is thus appropriate for the analysis of the experimental data. With the shearforce detection, d0 becomes known parameters, so that the model has only one adjustable parameter k  the apparent geometric parameters and Rg can be scrutinized a posteriori.

 

Figure 4. Experimental apparatus and schema of geometric parameters

Analysis Electrochemical Impedance Spectroscopy (EIS) provides significant information about the electrochemical behavior of Proton Exchange Membrane Fuel Cells (PEMFC). Nevertheless, the interpretation of the electrochemical responses remains complex without the use of physics-based models. In other hand if the EIS technic is well developed on small single cell, the literature does provide many data on model on stack operation. Therefore in order to describe the EIS behavior of (8 cells) stack, a 0D stirred tank model is used to analyze the experimental data. The results show a simple relation linking the two resistances difference with the air stoichiometry.

Figure 5. Experimental bench for PEM Stack and  experimental and fitted impedance diagrams of 5 celle

 

Performing accurate and reliable measurement of the area specific resistance (ASR) of electrode material is an important step for a lot of applications in the applied electrochemistry. The placement of working electrode, counter electrode and reference electrode in solid ionic conductor as less flexible as ionic liquid. It is also noticed that a non-symmetrical arrangement of RE and CE leads to significant errors on the recorded WE impedance. The simulations can be used to predict the deviation of electrical measurement from the real electrode impedance contribution and the computing can investigate that an optimum placement of RE exists for a fixed cell configuration



Figure 6. Schema of solid oxide sample half cells and their simulated and experimental impedance diagrams.

 

 

 

mise à jour le 1 février 2016

Univ. Grenoble Alpes