Title: Predictive control of commercial e-vehicle using a priori route information

Authors: Pavel Steinbauer; Josef Husák; Florent Pasteur; Petr Denk; Jan Macek; Zbynek Šika

Addresses: Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech Republic ' Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech Republic ' Siemens Industry Software S.A.S. DF PL STS CAE 1D, Lyon, France ' Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech Republic ' Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech Republic ' Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech Republic

Abstract: The driving range of the vehicle is usually an issue due to the limited energy storage capacity of the acu-pack. Thus, the e-vehicle control towards energy consumption decrease is of extreme importance. The known information about route properties can be used to plan torque/braking profile in an optimal way. Several approaches are compared. The first is design approach based on model predictive control (MPC) in combination with prior (before the trip starts) dynamic optimisation, the other is model-predictive control using hard limits based on route shape analyses and legal limits. The classical, optimised PID control is used as reference driver. A detailed driving range estimation model of a Fiat Doblo e-vehicle is the basis, including the main e-vehicle subsystem 1D model, e-motor, battery pack, air-conditioning/heating and EVCU. The model calibration is based on real vehicle measurements.

Keywords: e-vehicle; model predictive control; MPC; range extension; range estimation model.

DOI: 10.1504/IJPT.2018.090362

International Journal of Powertrains, 2018 Vol.7 No.1/2/3, pp.53 - 71

Received: 16 Jan 2017
Accepted: 31 May 2017
Published online: 13 Mar 2018 *

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