Prediction of Remaining Useful Life of Real-World Vehicle Lithium-Ion Power Battery Based on Aseq2seq-PF
Remaining useful life(RUL)prediction of lithium-ion power battery can estimate the future state of batteries,which can guide battery maintenance and reduce the risk of failure.The battery cycle conditions are not controlled in real-world vehicle conditions,and RUL prediction under dynamic operating conditions still suf-fers from difficulties in processing messy data,poor accuracy of prediction results and inability to take aging uncer-tainty into account.For this case,the Attention Mechanism Sequence to Sequence-Particle Filter(Aseq2seq-PF)hybrid model is proposed,where the common State of Charge(SOC)charging interval is selected to obtain the nor-malized battery capacity and a fusion prediction strategy of Iteration and Direct is adopted,with the Aseq2seq model as the Iteration part to achieve accurate prediction of capacity sequences,the PF model as the Direct part to achieve uncertainty prediction of capacity fluctuations,and RUL is predicted by extrapolating the trend of battery capacity degradation.Verified by the real-world vehicle power battery data,the public SOC charging interval effectively ob-tains a clear trend of capacity degradation.The hybrid model improves the long-term prediction accuracy of the ca-pacity degradation with good robustness,with reduction of average absolute error by more than 56%compared with existing models,and outputs confidence intervals to meet the needs of different application to achieve aging uncer-tainty description.
lithium-ion power batteryremaining useful life(RUL)real-world vehicle datasequence to sequenceaging uncertainty
NETL
NSTL
万方数据
维普
