State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradicti...State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradiction problem between the exact requirements of EKF(extended Kalman filter)algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period,a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed.The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery.Through the combination of data-based and model-based SOC estimation method,the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods,and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment(such as in an EV or PHEV).A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.展开更多
Qingdao is one of the essential growth poles in the process of new-type urbanization in Shandong Province. The study on the relationship between urban expansion and driving factors in this area is representative. This...Qingdao is one of the essential growth poles in the process of new-type urbanization in Shandong Province. The study on the relationship between urban expansion and driving factors in this area is representative. This paper examined urban expansion from the perspective of non-urban to urban conversion, detailing an empirical investigation into the spatiotemporal variations and impact factors of urban expansion in Qingdao. By using the Urban Expansion Intensity Index （UEII） and Urban Expansion Differentiation Index （UEDI）, the spatial and temporal difference of urban expansion in the Municipal District, Jiaozhou County, Jimo County, Pingdu County, Jiaonan County and Laixi County were calculated on a county unit data set for the period 1990 to 2008. A GIS and logistical regression models were applied for discussing the results of various factors in land use change. Results indicated that the elevation and slope factors showed negative effects to urban expansion. Distance to the city center and to road both also conferred negative effects. The population density and GDP were vital and positive factors of urban conversion. Neighborhood factors showed consistently positive effects. The magnitude of factors was various in different counties. A better understanding of the factors influencing land use change could support land use management and planning decisions.展开更多
Based on the global land cover data at 30 m resolution(Globe Land30) in the year 2000 and 2010, the urban expansion process of 320 cities in China was analyzed using lognormal regression, and the expansion model were ...Based on the global land cover data at 30 m resolution(Globe Land30) in the year 2000 and 2010, the urban expansion process of 320 cities in China was analyzed using lognormal regression, and the expansion model were established. Three metrics were presented for the models, including the peak position, the full width at half maximum, and the skewness. It was found that the three metrics could reveal different patterns of the urban expansion process of cities with different sizes. Specifically, cities with larger size tend to expand outward strongly, and their expansion intensity and influence are likely to be higher. Moreover, most cities' expansion occurs around the urban core with spatially limited influence. In addition, it was also found that the city's expansion intensity is related to the city size. These results showed that the lognormal regression model could describe the distribution of urban expansion with effectiveness and robustness.展开更多
基金Projects(51607122,51378350)supported by the National Natural Science Foundation of ChinaProject(BGRIMM-KZSKL-2018-02)supported by the State Key Laboratory of Process Automation in Mining&Metallurgy/Beijing Key Laboratory of Process Automation in Mining&Metallurgy Research,China+4 种基金Project(18JCTPJC63000)supported by Tianjin Enterprise Science and Technology Commissioner Project,ChinaProject(2017KJ094,2017KJ093)supported by Tianjin Education Commission Scientific Research Plan Project,ChinaProject(17ZLZXZF00280)supported by Tianjin Science and Technology Project,ChinaProject(18JCQNJC77200)supported by Tianjin Province Science and Technology projects,ChinaProject(2017YFB1103003,2016YFB1100501)supported by National Key Research and Development Plan,China
文摘State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradiction problem between the exact requirements of EKF(extended Kalman filter)algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period,a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed.The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery.Through the combination of data-based and model-based SOC estimation method,the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods,and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment(such as in an EV or PHEV).A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.
基金The Ministry of Land and Resources of Public Welfare Scientific Research(No.201411014-2)
文摘Qingdao is one of the essential growth poles in the process of new-type urbanization in Shandong Province. The study on the relationship between urban expansion and driving factors in this area is representative. This paper examined urban expansion from the perspective of non-urban to urban conversion, detailing an empirical investigation into the spatiotemporal variations and impact factors of urban expansion in Qingdao. By using the Urban Expansion Intensity Index （UEII） and Urban Expansion Differentiation Index （UEDI）, the spatial and temporal difference of urban expansion in the Municipal District, Jiaozhou County, Jimo County, Pingdu County, Jiaonan County and Laixi County were calculated on a county unit data set for the period 1990 to 2008. A GIS and logistical regression models were applied for discussing the results of various factors in land use change. Results indicated that the elevation and slope factors showed negative effects to urban expansion. Distance to the city center and to road both also conferred negative effects. The population density and GDP were vital and positive factors of urban conversion. Neighborhood factors showed consistently positive effects. The magnitude of factors was various in different counties. A better understanding of the factors influencing land use change could support land use management and planning decisions.
基金supported by the National High-Tech Research Program of China (Grant No. 2013AA122802)
文摘Based on the global land cover data at 30 m resolution(Globe Land30) in the year 2000 and 2010, the urban expansion process of 320 cities in China was analyzed using lognormal regression, and the expansion model were established. Three metrics were presented for the models, including the peak position, the full width at half maximum, and the skewness. It was found that the three metrics could reveal different patterns of the urban expansion process of cities with different sizes. Specifically, cities with larger size tend to expand outward strongly, and their expansion intensity and influence are likely to be higher. Moreover, most cities' expansion occurs around the urban core with spatially limited influence. In addition, it was also found that the city's expansion intensity is related to the city size. These results showed that the lognormal regression model could describe the distribution of urban expansion with effectiveness and robustness.
