RUL prediction of Li-ion battery based on relaxation voltage model
Li-ion battery exhibits a nonlinear degradation trend over long-term use.Predicting nonlinear degradation is crucial for extend battery life and ensure safety.A nonlinear degradation knee-point prediction method using relaxation voltage as a feature sequence is proposed,which enables joint prediction of knee-point and remaining useful life (RUL) .A framework for RUL prediction combining knee-point degradation features is established to improve prediction accuracy.The proposed joint prediction method is validated on different battery datasets using transfer learning,the mean absolute error for knee-point and RUL prediction is below 26 cycles,the root mean squared error is below 28 cycles.This method uses relaxation voltage to predict knee-point and RUL,thereby indirectly predicting the state of health (SOH) of battery,with advantages in prediction accuracy and broad applicability.
Li-ion batteryremaining useful life (RUL)knee-point predictionrelaxation voltagestate of health (SOH)
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