Abstract
We report a method to optimize the topology of porous electrodes without a prior assumption of the pattern. We take a two-dimensional NMC-Li cell as an example. The domain of NMC is discretized by a 5×5 mesh and the solid volume fraction is represented by 25 nodal variables. We use a deep-learning-boosted optimization algorithm to find the optimal material distribution that gives the maximum specific energy. The method produces an electrode pattern with 18% higher specific energy than that of a uniform electrode. This generic geometry optimization approach provides a powerful tool for the design and optimization of porous electrodes.
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