Search Results

Control of vehicle powertrain thermal management systems is becoming more challenging as the number of components is growing, and as a result, advanced control methods are being investigated. Model predictive control (MPC) is particularly interesting in this application because it provides a suitable framework to manage actuator and temperature constraints, and can potentially leverage preview information if available in the future. In previous SAE publications (2015-01-0336 and 2016-01-0215), a robust MPC control formulation was proposed, and both simulation and powertrain thermal lab test results were provided. In this work, we discuss the controller deployment in a vehicle; where controller validation is done through road driving and on a wind tunnel chassis dynamometer. This paper discusses challenges of linear MPC implementation related to nonlinearities in this over-actuated thermal system.

Numerous studies describe the fuel consumption benefits of changing the powertrain temperature based on vehicle operating conditions. Actuators such as electric water pumps and active thermostats now provide more flexibility to change powertrain operating temperature than traditional mechanical-only systems did. Various control strategies have been proposed for powertrain temperature set-point regulation. A characteristic of powertrain thermal management systems is that the operating conditions (speed, load etc) change continuously to meet the driver demand and in most cases, the optimal conditions lie on the edge of the constraint envelope. Control strategies for set-point regulation which rely purely on feedback for disturbance rejection, without knowledge of future disturbances, might not provide the full fuel consumption benefits due to the slow thermal inertia of the system.