A fast data-driven battery capacity estimation method under non-constant current charging and variable temperature

Abstract

Data-driven methods have been widely used in capacity estimation of lithium-ion batteries. Non-constant current charging and variable-temperature operating scenarios are inevitable in real applications. However, existing classical constant current charging based capacity estimation methods may not be applied to such scenarios. To this end, a data-driven method adapted to non-constant current charging is proposed in this work. A single health feature collected within 3 min is combined with Gaussian process regression (GPR) to achieve fast and transferable battery capacity estimation. To validate the effectiveness of the proposed method, ten batteries are performed for aging tests using a non-constant current charging protocol. The validation results show that the proposed method's average capacity estimation error (mean absolute percentage error) is only 0.65 %. The estimation error is reduced by more than 30 % for the new temperature condition. Compared with the three existing classical methods, the capacity estimation accuracy of the proposed method is improved by 69 %, 82 %, and 68 %, respectively. Additional experiments demonstrate the generality of the proposed approach for different battery chemistries and charging protocols. This work opens the door to battery capacity estimation for non-constant current charging scenarios.