Life prediction of on-board supercapacitor energy storage system based on gate recurrent unit neural network using sparse monitoring data

Abstract

With the increasing use of supercapacitor in transportation and energy sectors, service life prediction becomes an important aspect to consider. As the aging process of onboard supercapacitors is closely related to practical working conditions, the actual service life may be inconsistent with the cycle life measured in the laboratory. However, the low-quality onboard monitoring data recording the historical working conditions is usually sparse and fragmented, making it difficult to extract valuable information. In our previous study, we successfully obtained the characteristic parameters from sparse and fragmented data, whereas those characteristic parameters change periodically and couldn't be used directly for life prediction. In this paper, we firstly extract the degradation trend term of supercapacitor by a composite sine and polynomial time series decomposition model from the characteristic parameters. Secondly, in order to make up for the lack of data, a GRU network is designed to generate more sample data which is in consistent with historical data evolution trends. The combination of input characteristic variables including the extracted historical characteristic capacitance $C$, temperature T and the time fitting sequences $C\left(t_D\right)$ are selected to improve the accuracy of GRU predictions. The predictive error of the characteristic capacitance $C$ is 2.36 %. Finally, the life prediction of on-board supercapacitors based on actual working conditions is realized.