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In order to further improve the energy utilization of the range extender electric vehicle (REEV), the energy management strategy of the REEV under different heat demands and operating temperatures is studied. Firstly, for reducing the extra fuel consumption caused by engine operating temperature, the influence of engine lubricating oil temperature on the equivalence factor is analyzed, and a novel method is proposed, which can determine the equivalence factor by three parameters, including engine temperature, operating condition type, and battery state of charge (SOC). Secondly, to reduce the influence of frequent engine start/stop on fuel consumption, a method to establish the penalty factor based on battery SOC is proposed. Finally, the engine temperature rise law under low-temperature environment and the compressor drive mode determination method under high-temperature environment are studied according to the heat demand under different ambient temperatures. An adaptive equivalent consumption minimization strategy (ECMS) considering the effect of temperature under different heat demands is proposed on the basis of the above research. The simulation under the worldwide harmonized light vehicle test cycle (WLTC) drive cycle reveals that by using the adaptive ECMS, the vehicle operating cost is reduced by 5.69% in normal temperature environment, 0.53%-2.39% in a low-temperature environment, and 0.2%-2.01% in a high-temperature environment. [ABSTRACT FROM AUTHOR]

Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D0), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation. [ABSTRACT FROM AUTHOR]

The digital era has accelerated corporate innovation activity. Under the aim of "carbon neutrality", green technological innovation pushed enterprises toward resource conservation and reduced energy consumption. Although digital technology could increase the utilization of resources in the whole view, it remains unclear under what conditions the data affects green technological innovation performance (GTIP) effectively, and how data governance empowers different organizational management levels by delegating digital information in this progress. This study draws on the knowledge management theory and dynamic capability theory to explore the relationship between data governance and green technological innovation performance in firms. To examine our research model, annual reports of Chinese firms from 2013 to 2019 are analyzed. Results from Poisson fixed-effect model analysis indicate that data governance in general has no direct significant effect on green innovation performance, but for detail data governance items, green innovation reaches maximum, then decreases under the influence of decentralization of data governance, while a slightly U-shaped curve happens when centralization of data governance adopts, but these two curves are asymmetrical contradicted. Our analysis also highlights the important role of corporate environmental proactivity, as it positively moderates the relationship between two types of data governance and firm's green technological innovation performance. Besides, we examine this project by using three-dimensional plot method and Johnson-Neyman check, then find these regressions are robust in our analysis. Finally, implications and recommendations for data governance research and practices are presented. • Centralization of data governance shows slightly U-shaped relationship with GTIP. • Decentralization of data governance shows inverted U-shaped relationship with GTIP. • Corporate environmental proactivity moderates two non-linear relationships above. • Both granted patent and eliminated sample check support these results. [ABSTRACT FROM AUTHOR]

A significant amount of global electric power generation is produced from the combustion of fossil fuels. Steam boilers are one of the most important components for steam and electricity production. The objective of this paper is to establish a theoretical framework for the sustainability analysis of a utility boiler. These analyses can be used by decision-makers to diagnose and optimize the sustainability of a utility boiler. Seven utility boiler systems are analyzed using energy and embodied solar energy (emergy) principles in order to evaluate their environmental efficiencies. They include a subcritical coal fired boiler, a supercritical coal fired boiler, an oil fired boiler, a natural gas fired boiler, a concentrating solar power boiler utilizing a tower configuration, a biomass boiler, and a refuse derived fuel boiler. Their relative environmental impacts were compared. The results show that the natural gas boiler has significantly lower CO 2 emission than an equivalent coal or oil fired boiler. The refuse derived fuel boiler has about the same CO 2 emissions as the natural gas boiler. The emergy sustainability index of a utility boiler system is determined as the measure of its sustainability from an environmental perspective. Our analyses results indicate that the natural gas boiler has a relatively high emergy sustainability index compared to other fossil fuel boilers. Converting existing coal boilers to natural gas boilers is a feasible option to achieve better sustainability. The results also show that the biomass boiler has the best emergy sustainability index and it will remain a means to utilize the renewable energy within the Rankine steam cycle. Before solar boiler technology can be widely used in the United States’ utility power industry, its capital cost and the O&M cost should be reduced. Using the results of this research, decision-makers can make better-informed, environmentally-efficient selections of future utility scale boilers. [ABSTRACT FROM AUTHOR]