Simulation of Optimization and Utilization for LiB with Multi-Element Network

Wednesday, 4 October 2017
Prince George's Exhibit Hall D/E (Gaylord National Resort and Convention Center)
K. Lin (Kyoto University), G. Inoue (Kyushu University), and M. Kawase (Kyoto University)
Simulation of Optimization and Utilization for LiB with Multi Element Network

Lithium ion batteries (LiBs) are used in electric vehicles (EVs), hybrid electric vehicles (HEVs) and so on. However, it is required to have more power density and more energy density. In order to develop the performance of LiB, not only material characteristics such as active materials and electrolytes [1][2], but also optimizing electrode structure is required[3]. Therefore, chasing the state in electrode layer using the numerical computation is a critical measure for the comprehension of phenomenon in the cell. However, in the relatively micro-scale system such as the electrode layer, a slight difference in structure affects the battery performance with a slight difference in structure. However, usual simulations demand the reactive interface area and the tortuosity factor which critically affect the cell performance by reasonableness or approximation, as it might overlook the phenomenon from minute structure of electrode layer. The purpose of this study is to develop a numerical computation technique that reflected the micro characteristic of electrode structure directly by building a Multi Element Network Model based on simulation of porous structures.

This simulation first located active materials randomly in three-dimensional space. The active material particles are all spherical and overlap is not allowed. Next, assuming the space except active material particles as electrolyte phase, and located imaginary spheres with greatest diameter at positions fixed by 4 active material particles or more, which memorize electrolyte information such as ion electric potential at the position. After constructing electrode structure, the (active material) particle network and pore network were built as shown in Figure 1. The electronic conduction was calculated in particle network, as the ionic conduction and diffusion calculated with pore network, while the electrode reaction occurring at interface between active material particles and electrolyte. Finally, by applying this model to a galvanostatic discharge simulation based on the porous electrode theory [4][5], the state inside electrode layer was converged with iterative computation by taking the mass balance and electron balance of each particles and imaginary electrolyte spheres.

LiCoO2 and graphite were employed as cathode and anode active materials, respectively. Volume ratio of active material and the thickness of both electrode were set to be 0.5 , 10 µm respectively. The particle size according to normal distribution, which have a median diameter 2 µm. Figure 2 shows a calculated result of potential distribution in the cell when discharging at 100C discharge rate. However, because the calculation load in a primary stage of iterative computation, this is a result in which potential profile converged before time advanced. Compared with the overvoltage depending on the reaction or transport resistance, the overvoltage due to electron transport was largest which means the main resistance being the electron transport resistance in this case. This result proves that Multi Element Network makes it available to assign the main factor in electrode layer, and more evaluation of various electrode structure will be reported in poster.


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[2] K. Ishikawa, T. Sugawara, Y. Munakata, K. Kanamura, the 56th Battery Symposium in Japan(2015), 3E16.

[3] G. Inoue et al., ECS Trans., 25 (1), (2009), 1519-1527.

[4] M. Doyle, T. F. Fuller, J. Newman, J. Electrochem. Soc., 140(1993), 1526-1533.

[5] G. M. Goldin, A. M. Colclasure, A. H. Wiedemann, R. J. Kee, Electrochimica Acta, 64 (2012), 118-129.