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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]

Developing a common medication strategy for disease control and management could be greatly beneficial. Investigating the differences between diseased and healthy states using differentially expressed genes aids in understanding disease pathophysiology and enables the exploration of protein-drug interactions. This study aimed to find the most common genes in diarrhea-causing bacteria such as Salmonella enterica serovar Typhimurium, Campylobacter jejuni, Escherichia coli, Shigella dysenteriae (CESS) to find new drugs. Thus, differential gene expression datasets of CESS were screened through computational algorithms and programming. Subsequently, hub and common genes were prioritized from the analysis of extensive protein–protein interactions. Binding predictions were performed to identify the common potential therapeutic targets of CESS. We identified a total of 827 dysregulated genes that are highly linked to CESS. Notably, no common gene interaction was found among all CESS bacteria, but we identified 3 common genes in both Salmonella-Escherichia and Escherichia-Campylobacter infections. Later, out of 73 protein complexes, molecular simulations confirmed 5 therapeutic candidates from the CESS. We have developed a new pipeline for identifying therapeutic targets for a common medication strategy against CESS. However, further wet-lab validation is needed to confirm their effectiveness. [ABSTRACT FROM AUTHOR]