US 12,263,827 B2
Model-based predictive control of a vehicle taking into account a time of arrival factor
Valerie Engel, Markdorf (DE); Andreas Wendzel, Grünkraut (DE); Michael Wechs, Weißensberg (DE); Maik Dreher, Tettnang (DE); Lorenz Fischer, Friedrichshafen (DE); Oliver Schneider, Tettnang (DE); Christian Baumann, Friedrichshafen (DE); Edgar Menezes, Ravensburg (DE); and Felix Spura, Friedrichshafen (DE)
Assigned to ZF Friedrichshafen AG, Friedrichshafen (DE)
Appl. No. 17/776,946
Filed by ZF Friedrichshafen AG, Friedrichshafen (DE)
PCT Filed Mar. 5, 2020, PCT No. PCT/EP2020/055771
§ 371(c)(1), (2) Date May 13, 2022,
PCT Pub. No. WO2021/175423, PCT Pub. Date Sep. 10, 2021.
Prior Publication US 2022/0402476 A1, Dec. 22, 2022
Int. Cl. B60W 20/11 (2016.01); B60W 50/00 (2006.01); G01C 21/34 (2006.01)
CPC B60W 20/11 (2016.01) [B60W 50/0097 (2013.01); G01C 21/3469 (2013.01); B60W 2050/0037 (2013.01); B60W 2050/0075 (2013.01); B60W 2300/10 (2013.01); B60W 2300/125 (2013.01); B60W 2555/60 (2020.02); B60W 2556/50 (2020.02); B60W 2720/103 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A processor unit (3) for model-based predictive control of a vehicle (1) taking into account an arrival time factor, wherein the processor unit (3) is configured to:
calculate a trajectory for the vehicle (1) based at least in part on at least one arrival time factor, the trajectory including an entire route (20) to a specified destination (19) at which the vehicle (1) is to arrive, the at least one arrival time factor influencing an arrival time of the vehicle (1) at the specified destination (19); and
optimize a section of the trajectory for the vehicle (1) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm (13), the MPC algorithm (13) includes a longitudinal dynamic model (14) of a drive train (7) of the vehicle (1) and a cost function (15), the cost function (15) including:
a first term, the first term being an electrical energy predicted according to the longitudinal dynamic model (14) and weighted with a first weighting factor, wherein the electrical energy is provided within the sliding prediction horizon by a battery (9) for driving an electric machine (8) of the drive train (7); and
a second term, the second term being a driving time predicted according to the longitudinal dynamic model (14) and weighted with a second weighting factor, the driving time being required by vehicle (1) to cover an entire distance predicted within the sliding prediction horizon,
wherein the processor unit (3) is configured to execute the MPC algorithm (13) as a function of the first term and as a function of the second term to minimize the cost function and determine an input variable for the electric machine (8).