Remote self-learning of driving cycle for hybrid electric vehicle

This paper presents a remote self-learning method based on general packer radio service (GPRS) for driving cycle recognizing to hybrid electric vehicle. The system adopts an in vehicle device to acquire real-time driving data through CAN bus and communicate wirelessly with central server by GPRS network and INTERNET. A Self-Organized Feature Mapping network is introduced to classify real-time driving data as three types of typical driving cycles for hybrid electric vehicle. During data transmission, wireless communication quality is a vital factor for completeness of data. A Model-based control algorithm to solve wireless network congestion problem is realized depending on the characteristics of the communication quality. The test result shows that the communication process can be improved greatly and the communication quality is increased 30% at least by this algorithm. The work shows that remote self-learning method is effective to define and characterize driving cycle of hybrid electric vehicle. In the sample application, it is helpful to optimize energy management strategy of hybrid electric vehicle.