US 12,233,884 B2
Model predictive control of multiple components of a motor vehicle
Timon Busse, Munich (DE); Matthias Friedl, Friedrichshafen (DE); Timo Wehlen, Friedrichshafen (DE); Valerie Engel, Markdorf (DE); and Christian Baumann, Friedrichshafen (DE)
Assigned to ZF Friedrichshafen AG, Friedrichshafen (DE)
Appl. No. 17/776,903
Filed by ZF Friedrichshafen AG, Friedrichshafen (DE)
PCT Filed Nov. 14, 2019, PCT No. PCT/EP2019/081322
§ 371(c)(1), (2) Date May 13, 2022,
PCT Pub. No. WO2021/093953, PCT Pub. Date May 20, 2021.
Prior Publication US 2022/0402508 A1, Dec. 22, 2022
Int. Cl. B60W 10/08 (2006.01); B60W 10/18 (2012.01); B60W 50/00 (2006.01)
CPC B60W 50/0097 (2013.01) [B60W 10/08 (2013.01); B60W 10/18 (2013.01); B60W 2050/0022 (2013.01); B60W 2050/0031 (2013.01); B60W 2510/08 (2013.01); B60W 2510/18 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A system for model predictive control, MPC, of multiple components (18, 19) of a motor vehicle (1), the system comprising processor unit (3), wherein:
the processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a first component (18) of the motor vehicle (1) and of a second component (19) of the motor vehicle (1);
the first component (18) is operable with different values (h1, h2, h3) of a first operating parameter (20), and the second component (19) is operable with different values (y1, y2, y3) of a second operating parameter (24);
the MPC algorithm (13) comprises a cost function (15) to be minimized and a dynamic model (14) of the motor vehicle (1);
the dynamic model (14) comprises a loss model (27) of the motor vehicle (1);
the loss model (27) describes an overall loss of the motor vehicle (1);
the cost function (15) comprises a first term representing the overall loss of the motor vehicle (1);
the overall loss depends on a combination of operating values, which includes a first value (h1; h2; h3) of the first operating parameter (20) and a second value (y1; y2; y3) of the second operating parameter (24);
the processor unit (3) is configured for determining, by executing the MPC algorithm (13) as a function of the loss model (14), a combination of operating values that minimizes the first term of the cost function (15);
the first term comprises an electrical energy weighted with a first weighting factor and predicted according to the dynamic model (14), which is provided within a prediction horizon by a battery (9) of a drive train (7) of the motor vehicle (1) for driving an electric machine (8) of the drive train (7);
the cost function (15) comprises, as a second term, a driving time weighted with a second weighting factor and predicted according to the dynamic model (14), which the motor vehicle (1) requires in order to cover an entire distance predicted within the prediction horizon;
the processor unit (3) is configured for determining an input variable for the electric machine (8) by executing the MPC algorithm (13) as a function of the first term and as a function of the second term such that the cost function is minimized; and
wherein the processor unit (3), by executing a conversion software module, is configured for
controlling a first actuator (22) of the first component (18) such that the first actuator (22) is operated with a first actuator value (x1; x2; x3), as the result of which the first component (18) is operated with the first value (h1; h2; h3) of the first operating parameter (20) of that combination of operating values, by which the first term of the cost function (15) is minimized, and
controlling a second actuator (25) of the second component (19) such that the second actuator (25) is operated with a second actuator value (z1; z2; z3), as the result of which the second component (19) is operated with the second value (y1; y2; y3) of the second operating parameter (24) of that combination of operating values, by which the first term of the cost function (15) is minimized.