A Battery Capacity and RUL Prognostic Approach Based on ARIMA and PF

It is important to predict the future available capacity and the remaining useful life (RUL) of battery accurately for the stable driving of electric vehicles in long terms. Therefore, a predicting technique integrating ARMIA and PF are presented in the paper. The ARIMA learns the past ten available battery capacities, which is used to predict the next available capacity. Particle Filter (PF) builds a model based on the past long-term available capacities, which is used to predict the decline law of battery capacity and discover the RUL of battery. The method proposed in this paper is tested by several batteries of NASA. The mean absolute errors of battery5, battery6 and battery7 are 0.006871 Ah, 0.011197631 Ah, and 0.005769204 Ah, respectively. The RUL error of battery B5, B6 and B7 by PF algorithm are 9 cycles, 2 cycles and 6 cycles, respectively.

關鍵字

Battery ； Capacity ； RUL prognostic ； ARIMA ； PF

參考文獻

Liu J , Song K , Zhu C , et al. Ge/C nanowires as high-capacity and long-life anode materials for Li-ion batteries.[J]. ACS Nano, 2014, 8(7):7051-7059.

Pinson M B , Bazant M Z . Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging and Lifetime Prediction[J]. Journal of the Electrochemical Society, 2012, 160(2):A243-A250.

Newbold P . ARIMA model building and the time series analysis approach to forecasting[J]. Journal of Forecasting, 2010, 2(1):23-35.

Peterson S B , Apt J , Whitacre J F . Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization[J]. Journal of Power Sources, 2010, 195(8):2385-2392.

Ouyang M , Feng X , Han X , et al. A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery[J]. Applied Energy, 2016, 165(Mar.1):48-59.

國際替代計量

A Battery Capacity and RUL Prognostic Approach Based on ARIMA and PF

全文下載

主題瀏覽