Gaussian process-based prognostics of lithium-ion batteries and design optimization of cathode active materials

The increasing adoption of lithium-ion batteries (LIBs) in consumer electronics, electric vehicles, and smart grids poses two challenges: the accurate prediction of the battery health to prevent operational impairments and the development of new materials for high-performance LIBs. Characterized by their flexibility and mathematical tractability, Gaussian processes (GPs) provide a powerful framework for monitoring and optimization tasks. This study employs two GP-based techniques: co-kriging surrogate modelling and Bayesian optimization. The GP training data comes from the cycling performance test of five CR2032 cells with Ni contents of 0.0, 0.4, 0.5, 0.6, and 1.0 in their cathode active material LiNixMn2-xO4. The co-kriging surrogate predicts the capacity degradation profile of a cell by exploiting information from different cells. Bayesian optimization identifies new Ni compositions that can produce cells with high initial specific capacity and large cycle life. The study shows the predictive capabilities of the co-kriging surrogate when correlated data is available. Bayesian optimization predicts that a Ni content of 0.44 produces cells with an initial specific capacity of 103.4 +/- 3.8 mAh g(-1) and a cycle life of 595 +/- 12 cycles. Furthermore, the Bayesian strategy identifies other Ni contents that can improve the overall quality of the current Pareto front.

Automated summary

This study utilizes Gaussian processes (GPs) to predict and optimize the performance of lithium-ion batteries (LIBs) through co-kriging surrogate modeling and Bayesian optimization. The results show that co-kriging surrogate can accurately predict the capacity degradation profile of the battery, while Bayesian optimization can identify new Ni compositions with high initial specific capacity and large cycle life.