Search Results

More stringent Federal emission regulations and fuel economy requirements have driven the automotive industry towards more sophisticated vehicle thermal management systems to best utilize the waste heat and improve driveline efficiency. The final drive unit in light and heavy duty trucks usually consists of geared transmission and differential housed in a lubricated axle. The automotive rear axle is one of the major sources of power loss in the driveline due to gear friction, churning and bearing loss affecting vehicle fuel economy. These losses vary significantly with lubricant viscosity. Also the temperatures of the lubricant are critical to the overall axle performance in terms of power losses, fatigue life and wear. In this paper, a methodology for modeling thermal behavior of automotive rear axle with heat exchanger is presented. The proposed model can be used to predict the axle lubricant temperature rise.

Thermal development of automotive applications is a lot more complex than it used to be in the past. Specifically, for Plug-in Hybrid Electric Vehicles (PHEVs), all the sub-systems are so intertwined that it’s hard to analyze them as sub-systems only. A system level solution is needed for proper sizing of components. For early thermal development, a co-simulation method can ensure that we take into account the inter-dependency of all the thermal features in the car. As for example, a large PHEV battery may need to be passively cooled by refrigerant, which is also associated with the interior Heating, Ventilation, and Air Conditioning (HVAC) cooling system. For proper sizing of the condenser, chiller etc., one has to account for the battery cooling and cabin cooling as one system. There are also many thermal actuators on a PHEV, e.g. control valves, pulse-width-module (PWM) pumps, electric compressor, electric coolant heaters etc.

Automotive thermal systems are becoming complicated each year. The powertrain efficiency improvement initiatives are driving transmission and engine oil heaters into coolant network design alternatives. The initiatives of electrified and autonomous vehicles are making coolant networks even more complex. The coolant networks these days have many heat exchangers, electric water pumps and valves, apart from typical radiators, thermostat and heater core. Some of these heat exchangers, including cabin heaters deal with very small amount of coolant flow rates at different ambient conditions. This paper describes how viscosity can be a major reason for simulation inaccuracy, and how to deal with it for each component in the coolant network. Both experimental and computational aspects have been considered in this paper with wide range of ambient temperatures.

The demand for vehicles with electrified powertrain systems is increasing due to government regulations on fuel economy. The battery systems in a PHEV (Plug-in Hybrid-electric Vehicle) have achieved tremendous efficiency over past few years. The system has become more delicate and complex in architecture which requires sophisticated thermal management. Primary reason behind this is to ensure effective cooling of the cells. Hence the current work has emphasized on developing a “Physics based” thermal management modeling framework for a typical battery system. In this work the thermal energy conservation has been analyzed thoroughly in order to develop necessary governing equations for the system. Since cooling is merely a complex process in HEV battery systems, the underlying mechanics has been investigated using the current model. The framework was kept generic so that it can be applied with various architectures. In this paper the process has been standardized in this context.

More stringent Federal emission regulations and fuel economy requirements have driven the automotive industry toward more efficient vehicle thermal management systems to best utilize the heat produced from burning fuel and improve driveline efficiency. The greatest part of the effort is directed toward the hybridization of automotive transmission systems. The efficiency and durability of hybrid powertrain depends on the heat generation in electric motors and their interactions among each other, ambient condition, the cooling system and the transmission component configuration. These increase the complexity of motor temperature prediction as well as the computational cost of running a conjugate heat-transfer based CFD analysis. In this paper, 1D physics based thermal model is developed which allows rapid and accurate component-wise temperature estimation of the electric motor during both steady-state and transient driving cycles.

Designing an efficient transient thermal system model has become a very important task in improving fuel economy. As opposed to steady-state thermal models, part of the difficulty in designing a transient model is optimizing a set of input parameters. The first objective in this work is to develop an engine compatible physics-based 1D thermal model for fuel economy and robust control. In order to capture and study the intrinsic thermo-physical nature, both generic “Three Mass” and “Eight Mass” engine models are developed. The models have been correlated heuristically using Simulink. This correlation and calibration process is challenging and time consuming, especially in the case of the 8-mass model. Hence, in this work a Particle Swarm Optimizer (PSO) method has been introduced and implemented on a simple 3-mass and more complex 8-mass engine thermal model in order to optimize the input parameters.

More stringent federal emission regulations and fuel economy requirements have driven the automotive industry toward more sophisticated vehicle thermal management systems in order to best utilize the waste heat and minimize overall power consumption. With all new technologies and requirements, how to properly design, optimize, and control the vehicle thermal and cooling systems become great challenges to automotive engineers. Model based approach has become essential to the new thermal management system architectures design and evaluation of the optimal system solutions. This paper will discuss how the model based vehicle thermal system simulation tools have been developed from analytical & empirical data, and have been used for assessment and development of new thermal management system architectures.

Simulation has become an integral part in the design and development of an automotive air-conditioning (AC) system. Simulation is widely used for both system level and component level analyses and are carried out with one-dimensional (1D) and Computational Fluid Dynamics (CFD) tools. This paper describes a 1D approach to model refrigerant loop and vehicle cabin to simulate the soak and cool down analysis. Soak and cool down is one of the important tests that is carried out to test the performance of a heating, ventilation and air-conditioning (HVAC) system of a vehicle. Ability to simulate this cool down cycle is thus very useful. 1D modeling is done for the two-phase flow through the refrigerant loop and air flow across the heat exchangers and cabin with the commercial software AMESim. The model is able to predict refrigerant pressure and temperature inside the loop at different points in the cycle.

Vehicle front end air flow management affects many aspects of vehicle aero/thermal performances. The HVAC system capacity is greatly driven by the airflow and the air temperature received at the condenser. In this paper, front end design practices are investigated using computer simulation and full vehicle test to evaluate their effects on AC system performance. A full vehicle 3D CFD model is developed and used to predict the airflow and temperature in underhood and around the vehicle body, and specifically the conditions entering the condenser. The condenser inlet airflow and temperature profiles from 3D CFD model are then used as inputs for the 1D AC system model. The 1D AC system model, which includes condenser, compressor, evaporator and TXV (Thermal eXpansion Valve), is developed to observe the critical AC performance indicators such as panel out air temperature and compressor head pressure.

Due to increased demand for improved fuel economy and reduction in CO2 emissions, active grille shutter (AGS) has been considered as one option to increase fuel economy by reducing vehicle drag resistance. An AGS system will allow airflow through the grille when demand on cooling system or air conditioning system is high. Under conditions of light load and moderate ambient temperatures and humidity, the grille does not have to be fully open. A reduction in the effective grille size opening can be achieved by either partially or fully closing the grille through a stepped speed motor actuator. When the grille opening size is reduced, under-hood airflow will decrease. Therefore, the operating points for the grille shutter should take into account the effect of temperature rise for under-hood and underbody components and the performance of the cooling and climate control systems.

A process to create a vehicle resistance curve based on airflow predictions using Computational Fluid Dynamics (CFD) simulation technique is presented. 1-dimensional engine cooling system simulation tool KULI is used to compute the coefficients of vehicle resistance curve. A full factorial Design of Experiment (DOE) established the relationship between the coefficients and the sum of absolute difference between KULI and CFD predictions. The NLPQL optimization routine is used to accurately predict the coefficients so that sum of absolute difference between KULI and CFD predictions is minimized.

This paper identifies the difference in powertrain cooling system content levels using a nominal and a +3 Standard deviation maximum temperature design approach. Variation simulation analysis tools are used along with a 1-D cooling system performance model to predict resulting temperature distribution for different combinations of input variable populations. The analysis will show differential in powertrain cooling system content, mass, and impact to fuel economy for a nominal vs. +3 sigma design approach.

This paper explores the trade-off between reliability-based design and robustness for an automotive under-hood thermal system using the iSIGHT-FD environment. The interaction between the engine cooling system and the heating, ventilating, and air-conditioning (HVAC) system is described. The engine cooling system performance is modeled using Flowmaster and a metamodel is developed in iSIGHT. The actual HVAC system performance is characterized using test bench data. A design of experiment procedure determines the dominant factors and the statistics of the HVAC performance is obtained using Monte Carlo simulation (MCS). The MCS results are used to build an overall system response metamodel in order to reduce the computational effort. A multi-objective optimization in iSIGHT maximizes the system mean performance and simultaneously minimizes its standard deviation subject to probabilistic constraints.

Today automotive thermal systems development is a joint effort between an OEM and its suppliers. This paper presents a pilot program showing how OEMs and suppliers can jointly develop a reliable and robust thermal system using CAE tools over the internet. Federated Intelligent Product Environment (FIPER) has been used to establish B2B communication between OEMs and suppliers. Suppliers remotely run thermal systems computer models at the OEM site using the FIPER B2B feature.

The objective of this work is to perform a computer model based sensitivity analysis of parameters of an automotive air conditioning system to identify the critical parameters. Design of Experiment (DOE) and Analysis of Variance (ANOVA) techniques have been used to identify the critical parameters and their relative effects on the air conditioning system performance. The sensitivity analysis has been verified by running similar tests on an air conditioning system test stand (AC Test Stand).

A 1-Dimensional model was developed to simulate the performance of an automotive Air Conditioning (AC) system. Its architecture and validation with vehicle test data (over a wide range of environmental and engine load conditions) are presented in this paper. This study demonstrates the ability of a simplified AC model to capture real system phenomena. Its sensitivity and limitations are evaluated, along with its potential as a system design tool.

This paper describes the use of robust engineering in engine cooling system design. 1-D thermal-fluid network simulation software has been used in conjunction with statistical variation analysis software. It shows how we can increase the reliability of an automotive engine cooling system by considering the variations of all factors of design. Design of experiment (DOE) and Monte Carlo simulation techniques have been used to optimize the engine cooling system design.