Novel Multiphysics Model for Advanced Material Solutions for Safer and Long-Lasting High Capacity Cobalt Free Batteries for Stationary Storage Applications (CoFBAT)

To optimize energy storage new battery technologies are required.[1,2] The objective of the CoFBAT European project is to develop a new generation of batteries for storage applications. The goal of the CoFBAT battery technology is to achieve a longer lifetime (up to 10000 cycles), lower costs (down to 0.03 €/kWh/cycle), improved safety and more efficient recycling (&gt”;”50 %).

Designing new battery technologies with improved characteristics requires the development of new battery materials. In the CoFBAT project high capacity anodes (TNO, SiC and graphite) are combined with a cobalt-free high voltage cathode (LNMO). The safety of the battery is significantly improved by using a gel polymer electrolyte (GPE) instead of a liquid equivalent.[3]

Nowadays, battery development and optimization are executed via experimental trial-and-error procedures. Consequently, battery development is an extremely costly and time-intensive process. Applying electrochemical models to design and test battery technologies can significantly reduce this processing time.[4] Computational modeling can be used to predict the performance of a designed battery cell and thus suggest a desired cell formulation to meet the requirements of a certain battery application, prior to manufacturing a prototype.[4,5]

The Newman-Doyle Pseudo-two-dimensional (P2D) model uses porous electrode theory and concentrated solution theory.[5–7] The P2D model is further simplified by using volume averaging technique and treating the electrodes as a homogeneous medium. The homogeneity simplifications assume that the active material particles in the electrodes are identical spherical particles and are uniformly distributed within the electrodes.[5,8]

Accurate model parametrization is crucial for efficient and reliable electrochemical model-based predictions. Cell analysis is time-consuming and requires specific measuring equipment. Multiple parameter determinations are bottlenecks for modelling purposes.

The preliminary results of the developed model show good agreement with the experimental results. Differences in voltage ranges can be caused by the model assumptions or by minor errors in the parameter characterization. The developed battery cells show very good capacity retention, with increasing capacity losses for higher C-rates.

Future works in the CoFBAT modeling framework at coin cell level include simulating different anode compositions, characteristic parameters of electrodes and the electrolyte formulation to obtain an optimal battery design. The effects of fast charging will also be simulated. Next, the model will be expanded to a 2D model for monolayer and multilayer pouch cell simulations. In these 2D models, the particle size distribution in the electrodes will be accounted for via heterogeneous electrode phases. Furthermore, different ageing mechanisms that were experimentally observed will be added to the simulations to obtain an accurate model of the final CoFBAT battery.

[1] BATTERY 2030+ Roadmap D2.4 2 Document Classification Deliverable Number & Title D2.4-BATTERY 2030+ roadmap Work Package WP 2-Roadmapping. 2020.

[2] Tian Y, Zeng G, Rutt A, Shi T, Kim H, Wang J, et al. Promises and Challenges of Next-Generation “beyond Li-ion” Batteries for Electric Vehicles and Grid Decarbonization. Chem Rev 2021″;”121:1623–69. https://doi.org/10.1021/acs.chemrev.0c00767.

[3] Chen S, Wen K, Fan J, Bando Y, Golberg D. Progress and future prospects of high-voltage and high-safety electrolytes in advanced lithium batteries: From liquid to solid electrolytes. J Mater Chem A Mater 2018″;”6:11631–63. https://doi.org/10.1039/c8ta03358g.

[4] Smekens J, Paulsen J, Yang W, Omar N, Deconinck J, Hubin A, et al. A Modified Multiphysics model for Lithium-Ion batteries with a LixNi1/3Mn1/3Co1/3O2 electrode. Electrochim Acta 2015″;”174:615–24. https://doi.org/10.1016/j.electacta.2015.06.015.

[5] Liu K, Gao Y, Zhu C, Li K, Fei M, Peng C, et al. Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries. Control Eng Pract 2022″;”124. https://doi.org/10.1016/j.conengprac.2022.105176.

[6] Doyle M, Newman J. Comparison of Modeling Predictions with Experimental Data from Plastic Lithium Ion Cells Acknowledgment. J Electrochem Soc 1996″;”143:1890–903.

[7] Comsol. The Battery Design Module User’s Guide. 2020.

[8] Xia L, Najafi E, Bergveld HJ, Donkers MCF. A Computationally Efficient Implementation of an Electrochemistry-Based Model for Lithium-Ion Batteries. IFAC-PapersOnLine 2017″;”50:2169–74. https://doi.org/10.1016/j.ifacol.2017.08.276.