Supercapacitors with advantages of high-power density, fast charging speed and long cycle life, have very promising application prospects in many fields such as transportation and energy storage. Usually, the onboard operation profiles will be recorded and sent to remote monitoring terminal for further study. As the working condition of onboard supercapacitor is complex and fluctuating, it requires high sampling frequency, accuracy, and continuous recording. However, the remote monitoring data usually has the problems of low sampling frequency and fragmented recording, making it difficult to extract the characteristic parameters by traditional parameter identification methods. To solve the problem, this paper makes an extensive investigation on the long-term remote monitoring data of a supercapacitor tram and proposes a set of data processing method that can extract the characteristic parameters of supercapacitors from the sparse and fragmented data. Firstly, a group of proper data segments considering thermal stability of supercapacitor system was selected as effective data from the fragmented daily monitoring data. Secondly, the parameters which can be used to study the aging indicators are extracted by interior point method. With this method, the simulated voltage error is within 0.3% with a sampling frequency of 0.1 Hz. Through analyzing 3.5 years of remote monitoring data, it is found that the characteristic parameters exhibit the feature of seasonal fluctuations, which is highly related to temperature. To further extract the aging trend, a linear fitting model which eliminates the effect of seasonal fluctuations is proposed which can be used for analyzing the evolution characteristic of the studied supercapacitor system.

Abstract. Ice flow velocity over long time series in Greenland plays a vital role in estimating and predicting the mass balance of the Greenland Ice Sheet and its contribution to global sea level rise. However, there are few Greenland ice flow velocity products with large spatial coverage available showing the Greenland ice flow velocity pattern before the 1990s. We proposed three methods, including parallax decomposition, grid-based NCC image matching, feature and gird-based image matching with constraints for estimation of surface velocity in East Antarctica based on ARGON KH-5 and LANDSAT imagery, and a systematic compilation method for the ice surface velocity in East Antarctica from the 1960s to 1980s. Based on the above methods and cartographic processes, this study designed a framework for the mapping of the historical ice flow velocity of the Greenland ice sheet. Currently, the early LANDSAT images covering several glaciers in North Greenland and Northwest Greenland have been processed and applied to velocity mapping using the cartographic process proposed in this study, and some preliminary results have been obtained.