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In order to meet the requirements of variable transmission ratios and improve the bearing capacity of gear teeth, a new type of double-circular-arc tooth profile elliptical gear is proposed based on the meshing theory and the structural parameter characteristics of the double-circular-arc tooth circular profile curve. The design method of its pitch curve is described. A three-dimensional model of the double-circular-arc tooth circular profile elliptical gear is established using SolidWorks software. Adams software is used to simulate the kinematics of the elliptical gear pair model with double-circular-arc tooth profiles, and the influence of different eccentricities on the variation rule of the double-circular-arc gear pair transmission ratio is analyzed; compared with the theoretical transmission ratio curve, the influencing factors of the fluctuation error of the simulation transmission ratio curve are analyzed. In order to reduce the vibration pulse of the gear pair, the appropriate value range of eccentricities is given. The design method and analysis results in this study have theoretical reference value for parametric design of non-circular gears with double-circular-arc tooth profiles, and can provide a basis for NC manufacturing and application of elliptic gear pairs with double-circular-arc tooth profiles.

In order to avoid the problem of too many AC energy consumption cycles and time-consuming for the standard electric vehicle driving range test, the DC energy consumption test can be used to obtain the energy consumption of the test cycle at any time period and estimate the driving range. However, the rationality of intercepting part of the circulating DC energy consumption to estimate the whole process still lacks sufficient experimental support. Therefore, in this paper, two different electric vehicle prototypes were selected, and a complete energy consumption test experiment was carried out according to the standard test process. Through the test of DC energy consumption, the difference characteristics of electric vehicle driving braking energy and energy consumption under repeated cycles were analyzed. The fluctuation range provides data support for the use of DC power to evaluate the energy consumption of electric vehicles

With the popularization of electric vehicles, more and more attention has been paid to how to select reasonable test methods to evaluate the driving range of electric vehicles. This paper selects two electric vehicle prototypes with different configurations to test the energy consumption of the shortened test cycle at normal temperature and the standard test cycle at low temperature, and analyzes the constant speed working condition in the shortened method and the CLTC-P working condition at low temperature. The results show that both the 100km/h constant speed working condition and the low temperature working condition will cause a significant increase in the energy consumption of electric vehicles, and the energy consumption difference of repeated cycles under the low temperature test condition will also increase significantly compared with that at room temperature. The relevant results are more A comprehensive understanding and evaluation of the energy consumption characteristics of electric vehicles provides a useful reference.

The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of online detection, we propose the introduction of a soft measurement strategy. First, due to the significant fluctuations in the production process variables and inter-variable coupling, comprehensive grey correlation analysis and kernel principal component analysis are employed to reduce the input dimension and computational complexity of the data, enhancing the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM), with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently, an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal parameters of the LSSVM network. Lastly, on-site industrial data validation indicates that the new model offers superior tracking capabilities and heightened accuracy. It is deemed aptly suitable for the online detection of mother liquor concentration. [ABSTRACT FROM AUTHOR]

Capacitive power transfer (CPT) is an attractive wireless power transfer (WPT) technology and it has been widely studied in many applications. Symmetrical structures and high-order compensation networks are always produced as optimization results and common configurations for high-efficiency CPT systems. However, in space-limited scenarios, an asymmetric structure tends to be a better choice. The related large number of high-order asymmetric system parameters is a key problem in parameter design. In this study, a general parameter design method that is based on reactive power optimization is proposed for an electric field resonance-based CPT system with an asymmetric six-plate coupler. The reactive power in the compensation network was analyzed and optimized under the constraint of transferred power. With equal reactive power, the optimization complexity was significantly reduced and the optimized system parameters were provided. To validate the effectiveness of the proposed method, a 1 MHz, 3.2 kW asymmetric CPT protype with 100 mm gap distance was implemented. The results indicate that, with the optimized parameters, high system efficiency can be achieved when the system's volume is reduced. At the rated power, about 95% DC–DC overall efficiency was achieved through a 6-pF coupling capacitor. [ABSTRACT FROM AUTHOR]

To address the significant soft measurement errors in traditional static models for evaporation process parameters, which are characterized by continuity and cumulativity, this paper proposes a dynamic correction method for soft measurement models of evaporation process parameters based on the autoregressive moving-average model (ARMA). Initially, the Powell's directional evolution (Powell-DE) algorithm is utilized to identify the autoregressive order and moving average order of the ARMA model. Subsequently, the prediction error of a mechanism-reduced robust least squares support vector machine ensemble model is utilized as input. An error time series prediction model, which compensates for the errors in the autoregressive moving average model, is then applied for dynamic estimation of the prediction error. Finally, an integration strategy using the entropy method is employed to combine the static soft measurement model, based on the mechanism-reduced robust least squares support vector machine, with the dynamic correction soft measurement model, which is based on the error time series compensation of the ARMA model. The new model is analyzed and validated using production data from an alumina plant's evaporation process. Compared to traditional models, the new model demonstrates significantly improved prediction accuracy and is capable of dynamic prediction of evaporation process parameters.