Remaining useful life estimation of lithium-ion cells based on k-nearest neighbor regression with differential evolution optimization

Abstract Remaining useful life estimation is of great importance to customers who use battery-powered products. This paper develops a remaining useful life estimation model based on k-nearest neighbor regression by incorporating data from all the cells in a battery pack. A differential evolution technique is employed to optimize the parameters in the estimation model. In this approach, remaining useful life is estimated from a weighted average of the useful life of several nearest cells that share a similar degradation trend to the cell whose remaining useful life needs to be estimated. The developed method obtains a remaining useful life estimation result with average error of 9 cycles, and the best estimation only has an error of 2 cycles. All of these estimations are done within 10 ms. Increasing the number of tested cells and nearest cells improves the estimation accuracy. The developed method reduces the estimation average error by 83.14% and 89.79% compared to particle filter and support vector regression, respectively. Therefore, estimation results and comparison validate the effectiveness of the developed method for remaining useful life estimation of lithium-ion cells.