Capacity Prediction Method of Lithium‐Ion Battery in Production Process Based on Improved Random Forest

Abstract

Measuring capacity in the grading process is an important step in battery production. The traditional capacity acquisition method requires considerable time and energy consumption; therefore, an accurate capacity estimation is crucial in reducing production costs. Herein, a capacity prediction method for lithium‐ion batteries based on improved random forest (RF) is proposed. This method extracts features from the voltage data of the entire formation process and the first 25% of the grading process, saving 56.7% of the energy consumption and 74.6% of the time in the grading process. The importance of these features is ranked by RF, the best feature subset is selected, and the RF algorithm with parameters optimized by the genetic algorithm is applied to establish a capacity prediction model. The results show that the proposed model can accurately predict the capacity, with root mean square error (RMSE) and mean absolute percentage error (MAPE) values of only 191.89 mAh and 0.13%, respectively. Compared with other regression algorithms, the model shows a lower RMSE and MAPE and a higher capacity prediction accuracy for low‐capacity cells.