Abstract: A method for predicting mass of heavy vehicles based on networked operating data and machine learning includes: collecting operating data; extracting speed, engine output torque, satellite elevation, and gear position under a driving condition; determining a transmission ratio of the heavy vehicle based on the gear position; filtering the speed, engine output torque, and the satellite elevation using three of the plurality of filtering parameters; determining a filtered vehicle longitudinal acceleration under the driving condition using the filtered speed, and one of the plurality of filtering parameters; determining a filtered road gradient sine value under the driving condition using the filtered speed, satellite elevation, and one of the plurality of filtering parameters; and inputting the speed, engine output torque, vehicle longitudinal acceleration, road gradient sine value, and transmission ratio into a vehicle mass prediction model to obtain a predicted mass.

Abstract: A method for predicting mass of heavy vehicles based on networked operating data and machine learning includes: collecting operating data; extracting speed, engine output torque, satellite elevation, and gear position under a driving condition; determining a transmission ratio of the heavy vehicle based on the gear position; filtering the speed, engine output torque, and the satellite elevation using three of the plurality of filtering parameters; determining a filtered vehicle longitudinal acceleration under the driving condition using the filtered speed, and one of the plurality of filtering parameters; determining a filtered road gradient sine value under the driving condition using the filtered speed, satellite elevation, and one of the plurality of filtering parameters; and inputting the speed, engine output torque, vehicle longitudinal acceleration, road gradient sine value, and transmission ratio into a vehicle mass prediction model to obtain a predicted mass.

Abstract: The present invention discloses a method for preparing surface-active onion-like carbon nanospheres based on vapor deposition, comprising: directly preparing high-surface-activity onion-like carbon nanospheres formed by coating ferroferric oxide nano-particles on an onion-like graphitized shell by taking liquid small organic molecule alkane n-dodecane as a carbon source to perform chemical vapor deposition at high temperature of 650˜700° C. in an inert carrier gas environment with existence of a ferrocene catalyst. An onion-like carbon nanosphere product prepared according to the present invention has good surface activity and thermal stability, is wide in practicability, and can be widely applied to the fields of adsorbing materials, energy storage materials, catalytic materials, medical materials and the like.

Abstract: The present invention discloses a method for preparing surface-active onion-like carbon nanospheres based on vapor deposition, comprising: directly preparing high-surface-activity onion-like carbon nanospheres formed by coating ferroferric oxide nano-particles on an onion-like graphitized shell by taking liquid small organic molecule alkane n-dodecane as a carbon source to perform chemical vapor deposition at high temperature of 650˜700° C. in an inert carrier gas environment with existence of a ferrocene catalyst. An onion-like carbon nanosphere product prepared according to the present invention has good surface activity and thermal stability, is wide in practicability, and can be widely applied to the fields of adsorbing materials, energy storage materials, catalytic materials, medical materials and the like.