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Idle Stop-and-go (ISG), also known as Auto Stop/Start, is a fuel saving technology common to many modern vehicles that enables the engine to shut down when the vehicle comes to a stop. Although it may help with fuel efficiency, many drivers in the North American market find the feature to be an annoyance due to hesitation in vehicle re-launch and engine shudder during stop or restart. This paper introduces the usage of traffic signal phase and timing (SPaT) information for controlling the activation of ISG with the goal of reducing driver complaints and increasing acceptance of the function. Previous studies proposed the utilization of Advanced Driver Assistance System (ADAS) to introduce adaptability in powertrain controls to traffic situation changes.

With the advancement of simulation software development, the efficiency of vehicle and powertrain controls research and development can be significantly improved. Traditionally, during the development of a new control algorithm, dyno or on-road testing is necessary to validate the algorithm. Physical testing is not only costly, but also time consuming. In this study, a virtual platform is developed to reduce the effort of testing. To improve the simulation accuracy, co-simulation of multiple software is suggested as each software specializes in certain area. The Platform includes Matlab Simulink, PTV Vissim, Tass Prescan and AVL Cruise. PTV Vissim is used to provide traffic environment to PreScan. PreScan is used for ego vehicle simulation and visualization. Traffic, signal and road network are synchronized in Vissim and PreScan. Powertrain system is simulated in Cruise. MATALB/Simulink serves as master of this co-simulation, and integrates the different software together.

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.

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.

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.

Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory’s FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls.