TY - JOUR
AU - Ma, Yan
AB - The remaining useful life (RUL) prognostic of lithium-ion batteries (LIBs) is important in the reliability of electric vehicles. The degradation state of LIBs is related to the current moment and historical data, which is a non-Markovian process with long-term dependence. This manuscript proposes a RUL prognostic approach based on a non-Markovian process, which uses a fractional Brownian motion (FBM) model. Firstly, a nonlinear FBM model is established to describe the battery non-Markovian capacity fading process. The drift parameter of the FBM model and degradation states are updated by an online Kalman filter when a new measurement value arrives. Then the maximum likelihood estimation approach is introduced to obtain the other undecided fixed parameters. This approach is based on off-line battery degradation historical data. According to the first hitting time, the probability distribution function is derived to quantify the uncertainty of the RUL prognostic results. Finally, two datasets are used to verify the effectiveness of the proposed method. For the NASA dataset battery #5, the relative errors of the RUL prediction results of the proposed method are 2.941 and 2.083 when the starting points of the predictions are 60 cycles and 80 cycles, respectively. Thus, the proposed method is superior to other methods.
TI - Remaining useful life prediction for lithium-ion battery using dynamic fractional brownian motion degradation model with long-term dependence
JF - Journal of Power Electronics
DO - 10.1007/s43236-022-00507-w
DA - 2022-12-01
UR - https://www.deepdyve.com/lp/springer-journals/remaining-useful-life-prediction-for-lithium-ion-battery-using-dynamic-NA4zOCYZbK
SP - 2069
EP - 2080
VL - 22
IS - 12
DP - DeepDyve
ER -