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Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

Accurate determination of driveshaft torque is desired for robust control, calibration, and diagnosis of propulsion system behaviors. The real-time knowledge of driveshaft torque is also valuable for vehicle motion controls. However, online identification of driveshaft torque is difficult during transient drive conditions because of its coupling with vehicle mass, road grade, and drive resistance as well as the presence of numerous noise factors. A physical torque sensor such as a strain-gauge or magneto-elastic type is considered impractical for volume production vehicles because of packaging requirements, unit cost, and manufacturing investment. This paper describes a novel online method, referred to as Virtual Torque Sensor (VTS), for estimating driveshaft torque based on Machine-Learning (ML) approach. VTS maps a signal from Inertial Measurement Unit (IMU) and vehicle speed to driveshaft torque.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone.

In this work we studied the effects of piston bowl design on combustion in a small-bore direct-injection diesel engine. Two bowl designs were compared: a conventional, omega-shaped bowl and a stepped-lip piston bowl. Experiments were carried out in the Sandia single-cylinder optical engine facility, with a medium-load, mild-boosted operating condition featuring a pilot+main injection strategy. CFD simulations were carried out with the FRESCO platform featuring full-geometric body-fitted mesh modeling of the engine and were validated against measured in-cylinder performance as well as soot natural luminosity images. Differences in combustion development were studied using the simulation results, and sensitivities to in-cylinder flow field (swirl ratio) and injection rate parameters were also analyzed.

The airflow that enters the front grille of a ground vehicle for the purpose of component cooling has a significant effect on aerodynamic drag. This drag component is commonly referred to as cooling drag, which denotes the difference in drag measured between open grille and closed grille conditions. When the front grille is closed, the airflow that would have entered the front grille is redirected around the body. This airflow is commonly referred to as cooling interference airflow. Consequently, cooling interference airflow can lead to differences in vehicle component drag; this component of cooling drag is known as cooling interference drag. One mechanism that has been commonly utilized to directly influence the cooling drag, by reducing the engine airflow, is active grille shutters (AGS). For certain driving conditions, the AGS system can restrict airflow from passing through the heat exchangers, which significantly reduces cooling drag.

Metal foam with their high porosity and heat storage capacity can be combined with phase change materials to be a powerful heat storage device. Numerical simulations of metal foam behavior can be challenging due to their complex geometric patterns necessitating high mesh requirements. Furthermore, simulations of the inner workings of a metal foam heat exchanger comprising of a large number of individual metal foam canisters can be impossible. The objective of the current work is to develop a computational model using a proprietary CFD tool Simerics-MP/Simerics-MP+® to simulate the workings of a metal foam heat exchanger with phase change element. A heat transfer coefficient capturing this heat transfer between wax and metal is used to formulate the “simplified” mixture model. The versatility of the proposed model is in the universality of its application to any shape or structure of metal foam. The computational model developed is tested to replicate the results of the 3D simulation.

There is an ongoing effort in the industry to develop an accelerated corrosion test for automotive heat exchangers. This has become even more important as automakers are focusing on corrosion durability of 15 years in the field versus current target of 10 years. To this end an acid immersion test was developed and reported in a previous paper for condensers (1). This paper extends those results to evaporators and establishes the efficacy of the test using these results and those reported in the literature. The paper also discusses variability in corrosion test results as observed in tests such as ASTM G85:A3 Acidified Synthetic Sea Water Test (SWAAT), and its relation to field durability.

Control of vehicle powertrain thermal management systems is becoming more challenging as the number of components is growing, and as a result, advanced control methods are being investigated. Model predictive control (MPC) is particularly interesting in this application because it provides a suitable framework to manage actuator and temperature constraints, and can potentially leverage preview information if available in the future. In previous SAE publications (2015-01-0336 and 2016-01-0215), a robust MPC control formulation was proposed, and both simulation and powertrain thermal lab test results were provided. In this work, we discuss the controller deployment in a vehicle; where controller validation is done through road driving and on a wind tunnel chassis dynamometer. This paper discusses challenges of linear MPC implementation related to nonlinearities in this over-actuated thermal system.

With the trending electrification of vehicle accessory drives brings new control concepts useful in many cases to optimize energy management within the powertrain system. Considering that direct engine drives do not have as much flexibility as independent electric drives, it is apparent that several advantages are to be expected from electric drives. New developed high efficient electric drives can be implemented when considering many vehicle sub-systems. Combinations of continuous varying and discrete flow control devices offer thermal management opportunities across several vehicle attributes including fuel economy, drivability, performance, and cabin comfort. Often new technologies are integrated with legacy systems to deliver maximum value. Leveraging both electrical and mechanical actuators in some cases presents control challenges in optimizing energy management while delivering robust system operation.

Hybrid Electric Vehicles (HEV) utilize a High Voltage (HV) battery pack to improve fuel economy by maximizing the capture of vehicle kinetic energy for reuse. Consequently, these HV battery packs experience frequent and rapid charge-discharge cycles. The heat generated during these cycles must be managed effectively to maintain battery cell performance and cell life. The HV battery pack cooling system must keep the HV battery pack temperature below a design target value and maintain a uniform temperature across all of the cells in the HV battery pack. Herein, the authors discuss some of the design points of the air cooled HV battery packs in Ford Motor Company’s current model C-Max and Fusion HEVs. In these vehicles, the flow of battery cooling air was required to not only provide effective cooling of the battery cells, but to simultaneously cool a direct current high voltage to low voltage (DC-DC) converter module.

High density polyethylene (HDPE) is widely used in automotive industry applications. When a specimen made of HDPE tested under cyclic loading, the inelastic deformation causes heat generated within the material, resulting in a temperature rise. The specimen temperature would stabilize if heat transfer from specimen surface can balance with the heat generated. Otherwise, the temperature will continue to rise, leading to a thermo assist failure. It is shown in this study that both frequencies and stress levels contribute to the temperature rise. Under service conditions, most of the automotive components experience low cyclic load frequency much less than 1 Hz. However, the frequency is usually set to a higher constant number for different stress levels in current standard fatigue life tests.

It is widely recognized in the automotive industry that, in very cold climatic conditions, the driving range of an Electric Vehicle (EV) can be reduced by 50% or more. In an effort to minimize the EV range penalty, a novel thermal energy storage system has been designed to provide cabin heating in EVs and Plug-in Hybrid Electric Vehicles (PHEVs) by using an advanced phase change material (PCM). This system is known as the Electrical PCM-based Thermal Heating System (ePATHS) [1, 2]. When the EV is connected to the electric grid to charge its traction battery, the ePATHS system is also “charged” with thermal energy. The stored heat is subsequently deployed for cabin comfort heating during driving, for example during commuting to and from work. The ePATHS system, especially the PCM heat exchanger component, has gone through substantial redesign in order to meet functionality and commercialization requirements.

The need to increase the fuel-efficiency of modern vehicles while lowering the emission footprint is a continuous driver in automotive design. This has given rise to the use of engines with smaller displacements and higher power outputs. Compared to past engine designs, this combination generates greater amounts of excess heat which must be removed to ensure the durability of the engine. This has resulted in an increase in the number and size of the heat exchangers required to adequately cool the engine. Further, the use of smaller, more aerodynamic front-end designs has reduced the area available in the engine compartment to mount the heat exchangers. This is an issue, since the reduced engine compartment space is increasingly incapable of supporting an enlarged rectangular radiator system. Thus, this situation demands an innovative solution to aid the design of radiator systems such that the weight is reduced while maintaining the engine within acceptable operating temperatures.

An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models. This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry.

Hybrid electric vehicles are continuously challenged to meet cross attribute performance while minimizing energy usage and component cost in a very competitive automotive market. As electrified vehicles become more mainstream in the marketplace, hybrid customers are expecting more attribute refinement in combination with the enhanced fuel economy benefits. Minimizing fuel consumption, which tends to drive hybrid powertrain engines to operate under lugging type calibrations, traditionally challenge noise, vibration, and harshness (NVH) metrics. Balancing the design space to satisfy the cost metrics, energy efficiency, noise and vibration & drivability under the hybrid engine lugging conditions can be optimized through the use of multiple CAE tools. This paper describes how achieving NVH metrics can put undesirable boundaries on Powertrain Operation which could affect other performance attributes.

An engine cooling system in an automotive vehicle comprises of heat exchangers such as a radiator, charge air cooler and oil coolers along with engine cooling fan. Typical automotive engine-cooling fan assembly includes an electric motor mounted on a shroud that encloses the radiator core. One of main drivers of fan shroud design is Noise, Vibration, and Harshness (NVH) requirements without compromising the main function of airflow for cooling requirements. In addition, there is also a minimum stiffness requirement of fan shroud which is often overlooked in arriving at optimal design of it. Low Speed Damageability (LSD) assessment of an automotive vehicle is about minimizing the cost of repair of vehicle damages in low speed crashes. In low speed accidents, these fan motors are subjected to sudden decelerations which cause fan motors to swing forward thereby damaging the radiator core. So designing fan shroud for low speed damageability is of importance today.

ERRATUM: In the article Reference 24 should read as follows: 24. Han, T., Booth, A., Song, S., Styles, D., and Hoard, J., “Review and A Conceptual Model of Exhaust Gas Recirculation (EGR) Cooler Fouling Deposition and Removal Mechanism” Proceedings of the Int. Conf. on Heat Exchanger Fouling and Cleaning, 2015”

Exhaust gas recirculation (EGR) coolers are used on diesel engines to reduce peak in-cylinder flame temperatures, leading to less NOx formation during the combustion process. There is an ongoing concern with soot and hydrocarbon fouling inside the cold surface of the cooler. The fouling layer reduces the heat transfer efficiency and causes pressure drop to increase across the cooler. A number of experimental studies have demonstrated that the fouling layer tends to asymptotically approach a critical height, after which the layer growth ceases. One potential explanation for this behavior is the removal mechanism derived by the shear force applied on the soot and hydrocarbon deposit surface. As the deposit layer thickens, shear force applied on the fouling surface increases due to the flow velocity growth. When a critical shear force is applied, deposit particles start to get removed.