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This study aims to solve the problem that both the speed and the required driving power of electric vehicles (EVs) will change during the dynamic wireless charging (DWC) process, making it difficult to charge EVs with a constant power considering the overall efficiency of DWC system, the numbers of EVs and the power supply capacity. Therefore, this paper proposes the power control and efficiency optimization strategies for multiple EVs.

The wireless power charging system for multiple loads with a structure of double-sided LCC compensation topology is established. The expressions of optimal transmission efficiency and optimal equivalent impedance are derived. Taking the Tesla Model 3 as an example, a method to determine the number of EVs allowed by one transmitter coil and the overall charging power is proposed considering EV speed, power supply capacity, safe braking distance and overall efficiency. Then, the power control strategy, which can adapt to the changes of EV speed and the efficiency optimization strategy under different numbers of EVs are proposed.

In this paper, a method to determine the numbers of EVs allowed by one transmitter coil and the overall charging power is proposed considering EVs speed, power supply capacity, safe braking distance and overall efficiency. The accuracy of the charging power is good enough and the overall efficiency reaches a maximum of 91.79% when the load resistance changes from 5Ω to 20Ω.

In this paper, the power control and efficiency optimization strategy of DWC system for multiple EVs are proposed. Specifically, a method of designing the number of EVs and charging power allowed by one transmitter coil considering the factors of EV speed, power supply capacity, safe braking distance and overall efficiency is designed. The overall efficiency of the experiment reaches a maximum of 91.79% after adopting the optimization strategy.

This paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal.

The paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved.

The paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM.

Because of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.

The paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.

This paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.

Twenty-two coal samples and eleven parting samples were taken from Gashun Coal Mine, Hoxtolgay coalfield, Xingjiang Autonomous Region, and the samples were analyzed by using optical microscopy. The results indicate that the dominant macerals are vitrinite (av. 76.6%) with minor amounts of inertinite (av. 18.84%) and low liptinite (av. 2.7%), along with low content of mineral matters (av. 1.86%). All GI, TPI, VI, GWI values and two facies diagrams indicate that the main coal facies are in limnic area. Meanwhile, the overall petrographic composition and coal facies types indicate that the coal formed in strong wet reducing peat accumulating conditions which were the lacustrine marshes.

The main purpose of this paper is to investigate the effect of performance appraisal mechanisms on employee productivity in public hospitals in Mbeya – Tanzania, when mediated by working environment and management style.

Using a sample of 338 employees, a cross-sectional design was adopted; questionnaires and interviews were used to collect primary data. Partial least squares structural equation modelling (PLS-SEM) was used to determine the relationship between the variables, and Sobel’s test was performed to test the mediation effects.

Astudy found a strong association between performance appraisal mechanisms and employee productivity (p < 0.05). Goal setting significantly enhanced employee productivity (p < 0.05). Performance planning negatively impacted employee productivity, especially when combined with the working environment (p < 0.05). Assessment criteria had a significant positive effect on employee productivity when mediated by working environment and management style (p < 0.001).

This is the first study to examine how a performance appraisal system influences the productivity of public hospital employees in Tanzania. The study further explores the potential mediating effects of the work environment and management style on this relationship. By analysing the interplay between these variables and their impact on productivity, organizations can develop targeted strategies to boost employee performance and achieve superior results.