Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction ...Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction framework for EVs,composed of four coordinated modules:data preprocessing,charging pattern classification,static prediction,and dynamic bias correction.First,raw charging data collected from the Battery Management System(BMS)is cleaned and normalized to address missing and abnormal values.An enhanced convolutional autoencoder(EV-CAE)is then employed to extract multi-scale temporal features,while K-Means clustering is used to identify representative charging behavior patterns.Based on the classified patterns,the static prediction module estimates the current charging duration by leveraging historical data and pattern labels.To enhance adaptability under dynamic conditions,a bias correction mechanism is designed,integrating linear,logarithmic,proportional,and deep learning-based strategies to adjust the prediction results in real time.Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy.In particular,the dynamic correction module increases the coefficient of determination(R^(2))from 0.948 to 0.960,while maintaining robust performance under fluctuating charging behavior and low-temperature conditions.展开更多
Based on the externality theory and the environmental value theory, the hypothesis of charging for waste dumping of open-pit metal mines was put forth. The charging methods were designed according to the characteristi...Based on the externality theory and the environmental value theory, the hypothesis of charging for waste dumping of open-pit metal mines was put forth. The charging methods were designed according to the characteristics of waste dumping of openpit metal mines, including charging based on the dumping amount of the total waste, multi-charging factors, exceeding standard punishment charging, and so on. The main charging parameter is based on the dumping area rather than the total amount of waste dumping. The charging model of waste dumping of open-pit mines was formulated, and the charging rate was divided into two parts, i.e., the standard charging rate and the differential charging rate. The standard charging rate was derived using the equilibrium dynamic model, whereas the differential one was obtained by establishing the fuzzy synthesized evaluation model.展开更多
In order to investigate detonation propagation characteristics of different charge patterns,the detonation velocities of superposition strip shaped charges made up of a detonating cord and explosives were measured by...In order to investigate detonation propagation characteristics of different charge patterns,the detonation velocities of superposition strip shaped charges made up of a detonating cord and explosives were measured by a detonation velocity measuring instrument under conditions of different ignition.The experimental results and theoretical analysis show that the maximum detonation propagation velocity depends on the explosive materials with the maximum velocity among all the explosive materials.Using detonating cord in a superposition charge can shorten detonation propagation time and improve the efficiency of explosive energy.The measurement method of detonation propagation velocity and experimental results are presented and investigated.展开更多
基金supported by Science and Technology Innovation Key R&D Program of Chongqing(CSTB2023TIAD-STX0024)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant number KJQN202201121).
文摘Accurate prediction of electric vehicle(EV)charging duration is critical for improving user satisfaction and enabling efficient real-time charging management.This paper proposes a dynamic charging duration prediction framework for EVs,composed of four coordinated modules:data preprocessing,charging pattern classification,static prediction,and dynamic bias correction.First,raw charging data collected from the Battery Management System(BMS)is cleaned and normalized to address missing and abnormal values.An enhanced convolutional autoencoder(EV-CAE)is then employed to extract multi-scale temporal features,while K-Means clustering is used to identify representative charging behavior patterns.Based on the classified patterns,the static prediction module estimates the current charging duration by leveraging historical data and pattern labels.To enhance adaptability under dynamic conditions,a bias correction mechanism is designed,integrating linear,logarithmic,proportional,and deep learning-based strategies to adjust the prediction results in real time.Experimental results on real-world EV datasets demonstrate that the proposed framework significantly improves prediction accuracy.In particular,the dynamic correction module increases the coefficient of determination(R^(2))from 0.948 to 0.960,while maintaining robust performance under fluctuating charging behavior and low-temperature conditions.
文摘Based on the externality theory and the environmental value theory, the hypothesis of charging for waste dumping of open-pit metal mines was put forth. The charging methods were designed according to the characteristics of waste dumping of openpit metal mines, including charging based on the dumping amount of the total waste, multi-charging factors, exceeding standard punishment charging, and so on. The main charging parameter is based on the dumping area rather than the total amount of waste dumping. The charging model of waste dumping of open-pit mines was formulated, and the charging rate was divided into two parts, i.e., the standard charging rate and the differential charging rate. The standard charging rate was derived using the equilibrium dynamic model, whereas the differential one was obtained by establishing the fuzzy synthesized evaluation model.
文摘In order to investigate detonation propagation characteristics of different charge patterns,the detonation velocities of superposition strip shaped charges made up of a detonating cord and explosives were measured by a detonation velocity measuring instrument under conditions of different ignition.The experimental results and theoretical analysis show that the maximum detonation propagation velocity depends on the explosive materials with the maximum velocity among all the explosive materials.Using detonating cord in a superposition charge can shorten detonation propagation time and improve the efficiency of explosive energy.The measurement method of detonation propagation velocity and experimental results are presented and investigated.
