Intelligent Vehicle Power Control Based on Prediction of Road Type and Traffic Congestions

摘要

This paper presents a machine learning approach to the efficient vehicle power management and an intelligent power controller (IPC) that applies the learnt knowledge about the optimal power control parameters specific to road types and traffic congestion levels to online vehicle power control. The IPC uses a neural network for online prediction of roadway types and traffic congestion levels. The IPC and the prediction model have been implemented in a conventional (non-hybrid) vehicle model for online vehicle power control in a simulation program. The benefits of the IPC combined with the predicted drive cycle are demonstrated through simulation. Experiment results show that the IPC gives close to optimal performances.