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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.

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.

Many leading companies in the automotive industry have been putting tremendous amount of efforts into developing new designs and technologies to make their products more energy efficient. It is straightforward to evaluate the fuel economy benefit of an individual technology in specific systems and components. However, when multiple technologies are combined and integrated into a whole vehicle, estimating the impact without building and testing an actual vehicle becomes very complex, because the efficiency gains from individual components do not simply add up. In an early concept phase, a projection of fuel efficiency benefits from new technologies will be extremely useful; but in many cases, the outlook has to rely on engineer’s insight since it is impractical to run tests for all possible technology combinations.

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.

It has been discussed in numerous prior studies that in-neutral coasting, or sailing, can accomplish considerable amount of fuel saving when properly used. The driving maneuver basically makes the vehicle sail in neutral gear when propulsion is unnecessary. By disengaging a clutch or shifting the gear to neutral, the vehicle may better utilize its kinetic energy by avoiding dragging from the engine side. This strategy has been carried over to series production recently in some of the vehicles on the market and has become one of the eco-mode features available in current vehicles. However, the duration of coasting must be long enough to attain more fuel economy benefit than Deceleration Fuel Cut-Off (DFCO) - which exists in all current vehicle powertrain controllers - can bring. Also, the transients during shifting back to drive gear can result in a drivability concern.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.

This paper introduces the advancement of Engine Idle Stop-and-Go (ISG, also known as Auto Engine Stop-Start) and Neutral Coasting Control (NCC) with utilizing Advanced Driver Assistance System (ADAS) and GPS. The ISG and the In-Neutral Coasting (also known as Sailing or Gliding) have been widely implemented in recent vehicles for improving their fuel economy. However, many drivers find them somewhat disturbing because they basically change behaviors of their cars from what they used to. This annoyance discourages usages of those functions and eventually undermines their benefit of fuel saving. In order to mitigate the problem, new ISG and NCC algorithms are proposed. As opposed to the conventional logics that rely only on driver’s pedal action, the new algorithms determine whether or not to enable those functions for the given driving condition, based on the traffic information obtained using ADAS sensors and the location data from GPS and navigation map.