Precise lifetime predictions for lithium‐ion cells are crucial for efficient battery development and thus enable profitable electric vehicles and a sustainable transformation towards zero‐emission mobility. However, limitations remain due to the complex degradation of lithium‐ion cells, strongly influenced by cell design as well as operating and storage conditions. To overcome them, a machine learning framework is developed based on symbolic regression via genetic programming. This evolutionary algorithm is capable of inferring physically interpretable models from cell aging data without requiring domain knowledge. This novel approach is compared against established approaches in case studies, which represent common tasks of lifetime prediction based on cycle and calendar aging data of 104 automotive lithium‐ion pouch‐cells. On average, predictive accuracy for extrapolations over storage time and energy throughput is increased by 38% and 13%, respectively. For predictions over other stress factors, error reductions of up to 77% are achieved. Furthermore, the evolutionary generated aging models meet requirements regarding applicability, generalizability, and interpretability. This highlights the potential of evolutionary algorithms to enhance cell aging predictions as well as insights.
Cycling Li-ion cells with large capacities requires high currents and hence an expensive measurement setup. Aging the Li-ion cell material in coin cells offers an orders-of-magnitude-lower power requirement to the battery tester. The preparation procedure used in this work allows one to build coin cells in a reproducible manner. The original 40 Ah pouch cells and the corresponding 4.3 mAh coin cells (PAT-Cell) utilizing electrode material from the original cells are cycled with 1C at different temperatures. The results show the same basic aging mechanisms in both cell types: loss of lithium inventory at room temperature but an increasing proportion of loss of active material toward higher temperatures. This is confirmed by similar activation energies in capacity degradation of the 40 Ah cells and the averaged coin cells. However, the capacity of the coin cells decreases faster over time. This is caused by diffusion of moisture into the coin cell housing. Nonetheless, the increasing water contamination over measurement time is not directly linked to the loss of capacity of the coin cells. Thus, the observed aging mechanisms of the 40 Ah cells can be qualitatively transferred to coin cell level.
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