E-vehicle predictive control for range extension

Abstract

The range is currently one of main drawbacks of e-mobility, as energy storage capacity is limited. On the other hand, various information and computational resources in the cloud can be used. The control scheme uses model based predictive controllers with hierarchy of prediction horizons with various lengths. A detailed range estimation model of a Doblo e-vehicle is basis with the main subsystems: vehicle 1D model, e-motor, battery pack, air-conditioning/heating and EVCU. Due to the system substantial nonlinearity, a broad grid of linearized model is selected to rebuild a piecewise linear model. Trajectory velocity profile, designed by cloud control layer serves as input. Resulting controllers are merged using gain scheduling approach.