Model Predictive Control for Engine Powertrain Thermal Management Applications 2015-01-0336

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.

A solution to this problem is to design a control strategy that utilizes an estimate of variability of future disturbances. In this work, we consider the design of a controller for direct optimization of fuel consumption which allows for improved handling of constraints on temperatures and actuators. We propose a robust Model Predictive Control (MPC) formulation to optimize fuel consumption. The controller formulation guarantees that temperature constraints are met for future load and speed profile within specified bounds. The performance of the proposed controller is demonstrated through simulations on a system that uses an electric water pump, active thermostat and radiator fan, to control engine oil and metal (lumped block/head mass) temperature during regular driving so that fuel consumption is minimized.