Chu Duanfeng

A design of brain-computer interface control platform for intelligent vehicle

It is always a hot spot of the brain-computer field to achieve various functions by utilizing electroencephalogram (EEG) to control external equipment. This paper describes a design of an intelligent vehicle control platform based on brain-computer interface (BCI), which obtains EEG through BCI, then simulation model is established by Matlab /Simulink to extract and classify EEG as control command, which will be sent to intelligent vehicle by wireless serial port communication, so as to realize the control of intelligent vehicle on roads of the sandbox. By imaging four status: left hand, right hand, right leg and resting, the platform can accomplish the real-time control of intelligent vehicle on road of the sandbox, including turning left, turning right, going forward and stopping. The findings of that experiment show that the control system has good feasibility and stability, which lays the foundation for the practical application of the control of intelligent vehicle via EEG signal. The accomplishment of this platform provides a new approach to expanding and improving human capability to control external equipment.

Research on SOC Estimation for Power Lithium Battery Based on Kalman Filter

Considering the lithium ion battery, this paper analyzes the influential factors of the SOC and the lithium battery model. Two lithium ion powered battery models are proposed. One is the battery equivalent circuit model - the Thevenin model, and another is the external characteristic of equivalent model - the algebra model. Utilizing Kalman filtering to estimate the SOC of the battery is described in detail. The simulation results show that if the error of the SOC initial value is large, the Kalman filter adjust the SOC value to converge close to the true value. This proves that Kalman filtering is an effective method.