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This paper presents the design, implementation, and performance evaluation of a reduced complexity algorithm for a predictive control which is based on our previously published SAE paper (2021-01-0225) titled, “Model Predictive Control for Engine Thermal Management System.” That paper presented a model predictive control (MPC) design concept and demonstrated energy efficiency improvements by enabling engine pre-cooling based on GPS/Navigation data to recognize future vehicle speed limit and road grade in anticipation of high engine load demand. When compared to conventional control, the predictive control demonstrated considerable energy and fuel savings due to delayed timing of both knock mitigation and activation of radiator cooling fan during high engine load demand. However, this predictive control strategy is much more complicated due to its highly coupled nonlinear behavior.

Hyundai-Kia Motor Company recently developed a multi-way, electrical coolant valve for engine thermal management module (TMM). The main purposes of the TMM are to boost fuel economy by accelerating engine warm-up and also to enhance engine thermal efficiency by actively controlling the operating temperature. In addition to those, the system can improve vehicle heating and cooling performance as well. The electrical coolant valve is a key component in the TMM as it modulates the amount of coolant flow to individual components in cooling system such as engine oil heat exchanger, heater core, and radiator. The coolant flow modulation is done by controlling the electric valve’s position with using an electric motor attached to the valve. The objective of the valve control is to manage coolant temperature at a desired level that varies depending on vehicle’s operating condition. This paper discusses the control algorithm developed for controlling electrical coolant valve.

Recently developed gasoline engines utilize more aggressive EGR rate to meet the emissions and fuel economy regulations. The EGR temperature is often estimated by the ECU and its accuracy affects the estimations of EGR flow rate and intake air flow rate and temperature. Therefore, the accuracy of EGR temperature estimation becomes more important than ever for precise EGR rate control. Typical lookup map based EGR cooler model without the sensitivity to the coolant flow rate is acceptable and widely used if the heat capacity of the coolant side is high enough. However, the coolant flow rate under real vehicle driving conditions often visit low-speed high-load part of the engine map where the lookup map based model suffers from the accuracy issues. This paper presents an investigation of the accuracy of the lookup map based model under different heat capacity conditions. In this study, a simple EGR cooler model based on effectiveness-NTU method was also developed.

A predictive control method for the cooling system of an engine is developed in order to improve fuel efficiency through the use of vehicle onboard GPS/Navigation system. Conventionally, in an internal combustion engine cooling system, coolant temperature is controlled from predefined maps or models depending on the engine speed, accelerator pedal position, engine torque, and/or fueling rate at that instant. Due to the instantaneous decisions taken to change target coolant temperature, road gradient changes in terrain could cause engine under-cooling on a steep uphill or over-cooling when driving downhill. The paper presents the concept of predictive coolant temperature control strategy, utilizing GPS/Navigation data to recognize driving conditions by sensing vehicle position, speed limit, and road information like elevation and grade.