A Novel Approach Investigating the Remaining Useful Life Predication of Retired Power Lithium-Ion Batteries Using Genetic Programming Method

Abstract

Abstract Electric vehicles (EVs) have been widely cherished by consumers in recent years. However, as the number of EVs continues to increase, the number of retired power batteries will also increase, especially retired power lithium-ion batteries (LIBs), which will cause serious energy waste. To reuse sufficiently retired power LIBs, we studied the remaining useful life (RUL) of the power LIBs after retirement, so that the battery can be used in different usage scenarios, such as electric bicycles, grid energy storage, and communication base stations. The study first considers the inconsistency of the internal resistance and capacity of the LIBs pack and uses the battery available energy to predict the RUL of the retired power LIBs. Then, we further use the genetic programming (GP) method to predict the RUL of retired power LIBs. The case study shows the prediction accuracy of GP is better than response surface methodology (RSM), Kriging, and radial basis function (RBF) surrogate model. When the LIBs cycles are 100, 110, 120, and 130, the GP model prediction is relatively accurate and the minimum prediction error is only 5.26%.