Abstract: The present invention relates to the field of intelligent vehicles, and more specifically, to a human-machine shared control method based on trajectory prediction. It comprises: establishing a road-vehicle model based on vehicle-related parameters and then designing a path-tracking controller; further predicting the driver's driving trajectory, evaluating the driving risk, and switching the control power between the automatic system and the human driver based on the driving risk. The present invention ensures that when the driver's inputs will cause high risk to the vehicle, the driver's input is deprived of; and that when the driver's driving inputs will not cause high risk to the vehicle, the driving is carried out by the driver, so that in the case where the automatic system is having a problem, the driver can effectively realize a safe intervention, and the safety of the intelligent driving is improved.

Abstract: A method for predicting sealing reliability of a soft packing lithium ion battery, includes steps of determining a key degradation mechanism, constructing a pressure-time model, determining a pressure-stress space model, obtaining a maximum peeling force-strength model, determining a maximum peeling force-time model, constructing a maximum peeling force-space model, and ultimately predicting sealing reliability of the soft packing lithium ion battery.