Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols

Advancing lithium-ion battery technology requires the optimization of cycling protocols.A new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life obtained by new cycling protocols using a single test lasting only 3 cycles, enabling rapid exploration of cycling protocol design spaces with orders of magnitude reduction in testing time.We achieve this by combining lifetime early prediction with a hierarchical Bayesian model (HBM) to rapidly predict performance distributions without the need for extensive repetitive testing.The methodology is applied to a comprehensive dataset of lithium-ironphosphate/graphite comprising 29 different fast-charging protocols.HBM alone provides high protocol lifetime prediction performance, with 6.5% of overall test average percent error, after cycling only one battery to failure.By combining HBM with a battery-lifetime prediction model, we achieve a test error of 8.8% using a single 3-cycle test.In addition, the generalizability of the HBM approach is demonstrated for lithium-manganese-cobaltoxide/graphite cells.