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Recently, all-solid-state-lithium-ion batteries have attracted attention as next-generation batteries serving as driving sources for electric vehicles (EVs) and hybrid electric vehicles (HEVs). However, it is required to have more power density and more energy density. In order to develop the performance of batteries, High-capacity negative active material (AM) such as Si have been developed, but their use is not easy due to severe expansion during charging and discharging [1]. Although materials with reduced expansion and contraction have been developed [2], it is unclear how much expansion of the AM is allowed in the first place and how much it affects the porous electrode structure. Also, since the performance of batteries depend not only on the material characteristics but also on its electrode structure, it is important to design an optimum electrode structure. 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. 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. Therefore, our laboratory has devised a multi-network model as a method that directly reflects the transport characteristics in the particle-packed structure [3]. In this study, we apply it to an all-solid-state battery and examine the effect of various structural factors on the expansion and contraction of the AM. This simulation first located AM particles randomly in three-dimensional space. The AM particles are all spherical and overlap is not allowed. Next, assuming the space except active material particles as solid electrolyte (SE) phase and located imaginary spheres with greatest diameter at positions fixed by 4 AM particles or more, which memorize electrolyte information such as ion electric potential at the position. After constructing electrode structure, the AM network and SE network were built as shown in Figure 1 The electronic conduction was calculated in AM network, as the ionic conduction calculated with SE network, while the electrode reaction occurring at interface between AM and SE. Finally, by applying this model to a galvanostatic discharge simulation based on the porous electrode theory [4], the state inside electrode layer was obtained with iterative computation by taking the mass balance and electron balance of each AM particles and SE particles. The structural change in the electrode layer due to the expansion and contraction of the AM was based on the previous research of this laboratory [5], and the discrete element method (DEM) was incorporated into the stress analysis. DEM is a method of calculating a force acting in a normal direction and a tangential direction between particles, assuming a spring representing elasticity and a dashpot representing viscous damping between particles in contact with each other. Using this, the interparticle stress in the electrode layer was calculated, and a dynamic change accompanying expansion and contraction of the AM was considered. With this model, it is possible to examine the effects of various structural factors and the optimal structural design in consideration of the expansion and contraction of the AM, and to provide guidelines for higher power density. Acknowledgment This research was supported by Grants-in-Aid for Scientific Research on Innovative Areas, “Science on Interfacial Ion Dynamics for Solid State Ionics Devices” MEXT, Japan FY2019-2023. Reference [1] X. Su et al., Adv. Energy Mater., 4, 1300882 (2014). [2] X. Zhang et al., Electrochemistry, 84 (6), 420 (2016). [3] K. Lin et al., ECS Transaction, 80（10）, 251–258 (2017). [4] G. M. Goldin et al, Electrochimica Acta, 64 , 118-129 (2012). [5] K. Ishikawa et al, the 56th Battery Symposium in Japan, 3E16 (2015). Figure 1 |
