With global non-renewable resources and environmental issues becoming more apparent, the development of new energy vehicles have become the trend of auto industry. Hybrid vehicle becomes the key development of new energy vehicles with its long distance, low pollution, low fuel consumption characteristics and so on. The battery performances directly influence the quality of the whole vehicle performance. Considering the importance of the battery state of charge (SOC) estimation and the nonlinear relationship between the battery SOC and the external characteristic, genetic algorithm (GA) and back propagation (BP) neural network are proposed. Because of the strong global search capability of the genetic algorithm and the generalization ability of BP neural network, the hybrid vehicle Ni-MH power battery GA-BP charging model is designed. In this approach, the network training speed is superior to the traditional BP network. According to the real-time data of the batteries, the optimal solution can be concluded in a short time and with high estimation precision.

For the problem of estimation sequence effect on channel estimation accuracy and echo cancellation effect, this paper, based on the basic principle of echo cancellation, analyses the effect of PN sequence mechanism and the correlation on the channel estimation parameters. Comparing with using the input signal itself as the estimation sequence. With the input signal OFDM, the results of simulation and actual operation show that the method can increase both the accuracy of channel estimation and echo cancellation effect effectively.