In order to save fuel and reduce emission of Hybrid Electrical Vehicle (HEV), a vehicle control strategy is proposed based on the system efficiency optimal. A vehicle performance simulation model has been built on Matlab/Simulink environment. The results show that, by using this vehicle control strategy, the dynamic performance and fuel economy of the vehicle are significantly improved compared with the traditional one. Finally, The vehicle control strategy has been verified by the bench test.

Energy control of HEV plays a very important role in the process of HEV design, which is directly related to the safety and feasibility. Considering the drive system of HEV is nonlinear and complex, a fuzzy control strategy which is combined with particle swarm optimization algorithm is designed to realize the energy control of HEV. Fuzzy control strategy does not need to built accuracy mathematics model and has good robustness, but it mostly depends on engineering experience and has poor ability of self-learning. So particle swarm optimization algorithm has been added to solve these disadvantages of fuzzy control strategy. In conclusion, this method can not only keep the advantage of fuzzy control strategy, but also has ability of self-learning and self-adapt because of particle swarm optimization added. And the simulation proves that this method is feasible and effective.

A reasonable and effective control strategy for HEV (Hybrid Electric Vehicle) with HESS (Hybrid Energy Storage System) can improve the system efficiency and battery service life. A dynamic programming-based global optimal control strategy which fully considered the efficiency of each component in HEV is presented. To compare with the nonlinear proportion factor dynamic coordination strategy, the fuel consumption is not increased. The cycle numbers of battery are further decreased which benefits its cycle life improvement. The regenerative energy callback ratio improves 3% under the shanghai drive cycle as an example. Since this optimization algorithm considers efficiency of each subsystem, the efficiency optimum under the selected driving cycle was realized. It can also provide a reference for the non-linear scaling factor allocation strategies to determine the scale factors.

Automotive airbag assembly process is complex and nonlinear, and one of its characteristics is that the accuracy of making the threshold comparison for fault diagnosis using field multi-sensor measured value is not high,. In this article, adopt self-organizing feature mapping network SOM to realize the fault diagnosis of automotive airbag assembly process, constitute the field function of SOM through wavelet functions, form sub-excitatory neuron to update weights, avoid SOM local optimum, so improve the accuracy of fault diagnosis of automotive airbag assembly process.

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