The working condition of the CID (current interrupt device) has an important impact on the safety of the prismatic lithium-ion batteries. One of the important factors that causes the failure of the prismatic power battery is the overturning of CID due to material creep. The tensile and creep tests of the MFX2 aluminum alloy which is the material of CID were designed. Based on the experimental data the isotropic hardening multilinear elastic-plastic constitution and creep constitution at the battery working temperature were established. The constitutions were also verified by the notch test. The finite element model of the CID and the cap was established to analyze the effect of creep on the failure behavior of the CID throughout life. The simulation tests in the constant temperature and constant load of CID is designed. The simulation results showed that the constitution characterizes the basic mechanical properties of the CID reasonably. During the life of the CID, the creep behavior would go through the fast stage firstly and then fall into the slow steady-state stage. The creep effect of the material would cause the CID to overturn in advance, overturning pressure would be reduced, the life of the power battery would also be reduced.

The tire forces of vehicles will fall into the non-linear region under extreme handling conditions, which cause poor path tracking performance. In this paper, a model predictive controller based on a nonlinear tire model is designed. The tire forces are characterized with nonlinear composite functions of the magic formula instead of a simple linear relation model. Taylor expansion is used to linearize the controller, the first-order difference quotient method is used for discretization, and the partial derivative of the composite function is used for matrix transformation. Constant velocity and variable velocity conditions are selected to compare the designed controller with the conventional controller in Carsim/Simulink. The results show that when the tire forces fall in the nonlinear region, two controllers have good stability, and the tracking accuracy of the controller designed in this paper is slightly better. However, after the tire forces become nonlinear, the controller with linear tire force becomes worse, the tracking accuracy is far worse than the controller with the nonlinear tire model, and the vehicle stability is also degraded. In addition, an active steering test platform based on LabVIEW-RT is established, and hardware-in-the-loop tests are carried out. The effectiveness of the designed controller is verified.

In current research of autonomous vehicle path tracking, most contributions focus on tracking accuracy during complex driving condition. However, the vehicle might fall into unstable dangerous condition when the road curvature is big or with high speed. A path tracking control framework is proposed in this paper, which considers path tracking accuracy and vehicle stability. In general, the control framework consists of three main components: vehicle stability judging controller, path tracking controller, and torque distributor. Firstly, the stability of the current vehicle can be evaluated by the stability judgment controller according to the phase plane of the sideslip angle. Then, the proportion of vehicle stability in the control objective is adjusted according to the current vehicle stability. To improve the path tracking accuracy, the path tracking is composed of a combined controller using MPC algorithm, which controls the front wheel angle and direct yaw moment at the same time. After that, the torque distributor is developed to distribute the desired yaw moment into four executive wheels, which synthesizes the results of power performance distribution and stability distribution. Finally, the effectiveness of the proposed control framework is verified by the Matlab-CarSim co-simulation experiment and the hardware-in-the-loop experiment based on LabVIEW-RT. The results show that the proposed controller has better tracking accuracy and stability than the general MPC controller.