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With the upcoming regulations for fuel economy and emissions, there is a significant interest among vehicle OEMs and fleet managers in developing computational methodologies to help understand the influence and interactions of various key parameters on Fuel Economy and carbon dioxide emissions. The analysis of the vehicle as a complete system enables designers to understand the local and global effects of various technologies that can be employed for fuel economy and emission improvement. In addition, there is a particular interest in not only quantifying the benefit over standard duty-cycles but also for real world driving conditions. The present study investigates impact of exhaust heat recovery system (EHRS) on a typical 1.2L naturally aspirated gasoline engine passenger car representative of the India market.

With the upcoming regulations for fuel economy and emissions, there is a significant interest among vehicle OEMs and fleet managers in developing computational methodologies to help understand the influence and interactions of various key parameters on Fuel Economy and carbon-di-oxide emissions. The analysis of the vehicle as a complete system enables designers to understand the local and global effects of various technologies that can be employed for fuel economy and emission improvement. In addition, there is a particular interest in not only quantifying the benefit over standard duty-cycles but also for real world driving conditions. Present study investigates impact of exhaust heat recovery system (EHRS) on a typical 1.2L naturally aspirated gasoline engine passenger car representative of the India market.

Vehicle development teams find it challenging to predict what their Heating, Ventilation and Air-Conditioning (HVAC) module performance will be for cold ambient (∼ -20 deg. C) test cycles such as defrost and cabin warm-up before the car is built. This uncertainty in predictions comes from varying engine heat rejection to coolant due to cold cylinder wall temperatures, calibration changes and degraded performance of various components within the cooling system such as the coolant pump owing to higher viscosity of the coolant. Measuring engine heat rejection at cold ambient is extremely difficult as the engine warms up as soon as it is fired. Multiple measurement points require long lead time to soak to the cold target temperature. It is a common practice to adjust engine calibration parameters to warm up coolant as fast as possible for an adequate defrost and cabin warm-up performance.

This paper discusses the sensitivity of key parameters that are used as an input into engine cooling system simulation model that affect the coolant temperature and required airflow calculations. In simulation, these parameters are obtained either from calculations of other programs such as a combustion program or from measured engine test data and are typically assumed to be constant. Tests and measurements from vehicle tests indicate that these parameters always vary affecting the final predicted coolant temperature. The sensitivity on few selected parameters such as the ambient pressure, temperature, humidity, coolant properties among others were studied. Results discussed in this paper quantify the effect of each of these parameters on required airflow and advise which parameters must be tightly controlled to improve the robustness of the simulation model and the accuracy of predictions.

This paper discusses a streamlined approach for characterizing the heat flows from the combustion chamber to the engine coolant, engine oil circuit and the ambient. The approach in this paper uses a built-in flow and heat transfer solver in the CAD model of the engine to derive heat transfer coefficients for the coolant-block interface, oil-block interface and the block-ambient interface. These coefficients take into account the changing boundary conditions of flow rate, temperatures, and combustion heat to help characterize the complex thermal interactions between each of these sub-systems during the warm-up process. This information is fed into a larger system model of the engine to get a more accurate prediction of the engine warm-up and the effect of various fuel economy improvement strategies being evaluated. One of the key benefits shared in this paper is the practicality of the process that can be replicated on every production vehicle simulation model.

This paper discusses simplified lumped parameter thermal modeling of power train components. In particular, it discusses the tradeoff between model complexity and the ability to correlate the predicted temperatures and flow rates with measured data. The benefits and problems associated with using a three lumped mass model are explained and the value of this simpler model is promoted. The process for correlation and optimization using modern software tools is explained. Examples of models for engines and transmissions are illustrated along with their predictive abilities over typical driving cycles.