Fast grading method based on data driven capacity prediction for high-efficient lithium-ion battery manufacturing

With the large-scale expansion of the battery market, the cost optimization of battery manufacturing has become a focus of attention. Among the complex production process of the battery, capacity grading requires a full discharge to measure the capacity and results in high cost. This study proposes a fast grading method in which the batteries are half discharged and graded according to the capacity predicted by a neural network. The prediction-based method takes half the time and saves about 37 % of the energy consumption. Twenty-three features are extracted as the initial features. The collinear features are screened-out, and three feature reduction methods are compared. Permutation importance can effectively clarify the nonlinear relationship and determine the critical features. The root-mean-square error of the testing set is 0.18 %. The method is feasible and has the potential for further evolution. The electrochemical mechanism involved in the features is analyzed by simulation. Under the guidance of the mechanism, the method can be transferred to other batteries.