A Rapid Test Method Based on Prediction with Swarm Intelligent Optimization and Monte Carlo Expansion for Lithium-Ion Battery Cycle Life

Abstract

The cycle life test offers significant sustainment for utilization and maintenance of lithium-ion batteries. The traditional way is continuous charge-discharge testing without interruption, which often takes one year or even longer. Therefore, this paper proposes a rapid cycle life test method based on intelligent prediction to replace the continuous test, which shortens the test period and accelerates product replacement. The original capacity data is decoupled into the short-term regeneration trajectory and the long-term degradation trajectory, which are predicted by the long-short term memory model optimized by swarm intelligent algorithm. The data expansion technique based on Monte Carlo sampling is used to increase the diversity of training data to improve the prediction accuracy. The feasibility and effectiveness are proved by NASA data sets. The results show that the cycle life test time reduced by at least 90% with the error less than 3 cycles.