Search Results

Due to stringent environmental requirements, the vehicle under-hood and underbody temperatures have been steadily increasing. The increased temperatures affect components life and therefore, more thermal protection measures may be necessary. In this paper, we present an algorithm for estimation of automotive component life due to thermal effects through the vehicle life. Traditional approaches consider only the maximum temperature that a component will experience during severe driving maneuvers. However, that approach does not consider the time duration or frequency of exposure to temperature. We have envisioned a more realistic and science based approach to estimate component life based on vehicle duty cycles, component temperature profile, frequency and characteristics of material thermal degradation. In the proposed algorithm, a transient thermal analysis model provides the exhaust gas and exhaust surface temperatures for all exhaust system segments, and for any driving scenario.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

In recent decades, there has been a growing focus on improving overall vehicle efficiency and fuel economy due to growing customer awareness and more stringent environmental regulations. Effort has been placed on improving the engine efficiency and reducing the losses of the transmission and driveline. One essential component of this process is to correctly size the transmission oil pump as it is one of the main energy consumers in the powertrain. Conversely, the oil pump has a critical mission of ensuring reliable and high quality gear shift as well as supplying lubrication and cooling oil to various components in the transmission. This paper outlines a strategy to systematically understand and quantify the main requirements for sizing the oil pump to ensure adequate performance while minimizing the energy consumption of the pump. The proposed framework is a three-legged approach.

A turbocharger is currently widely used to boost performance of an internal combustion engine. Generally, a turbocharger consists of a compressor which typically is driven by an exhaust turbine. The compressor will influence how the low frequency engine pulsation propagates in the intake system. The compressor will also produce broad-band flow induced sound due to the turbulence flow and high frequency narrowband tonal sound which is associated with rotating blade pressures. In this paper, a practical simulation procedure based on a computational fluid dynamics (CFD) approach is developed to predict the flow induced sound of a turbocharger compressor. In the CFD model of turbocharger compressor, the unsteady, moving wheel, detached eddy simulation (DES) approach are utilized. In this manner, both the broad-band and narrow-band flow induced sound are directly resolved in the CFD computation.

Rapid Compression Machines (RCM) offer the ability to easily change the compression ratio and the pressure/mixture composition/temperature to gather ignition delay data at various engine relevant conditions. Therefore, RCMs with optical access to the combustion chamber can provide an effective way to analyze macroscopic spray characteristics needed to understand the spray injection process and for spray model development, validation and calibration at conditions that are suitable for engines. Fuel surrogates can help control fuel parameters, develop models for spray and combustion, and perform laser diagnostics with known fluorescence characteristics. This study quantifies and evaluates the macroscopic spray characteristics of multicomponent gasoline surrogates in comparison to their gasoline counterparts, under gasoline direct injection (GDI) engine conditions.

As engines are equipped with an increased number of control actuators to meet fuel economy targets they become more difficult to control and calibrate. The large number of control actuators encourages the investigation of physics-based control strategies to reduce calibration time and complexity. Of particular interest is spark timing control and calibration since it has a significant influence on engine efficiency, emissions, vibration and durability. Spark timing determination to achieve a desired combustion phasing is currently an empirical process that occurs during the calibration phase of engine development. This process utilizes a large number of stored surfaces and corrections to account for the wide range of operating environments and conditions that a given engine will experience. An obstacle to realizing feedforward physics-based combustion phasing control is the requirement for an accurate and fast combustion model.

Increase in the atmospheric temperature across the globe during summer, increases the heat load in the vehicle cabin, creating a huge thermal discomfort for the passengers. There are two scenarios where these adverse conditions can be a problem during the summer. Firstly, while driving the vehicle in traffic conditions and secondly, when the vehicle is parked under the sun. When the vehicle is exposed to the radiation from the sun for a period, the cabin temperature can reach alarming levels, which may have serious discomfort and health effects on the people entering the vehicle. Although there are options of remote switching on of air conditioners, they are restricted to vehicles having an automatic transmission and availability of the mobile network. So, it is important to explore the possible options which can be used for restricting the external heat load to the cabin.

Component sizing generally represents a demanding and time-consuming task in the development process of electrified powertrains. A couple of processes are available in literature for sizing the hybrid electric vehicle (HEV) components. These processes employ either time-consuming global optimization techniques like dynamic programming (DP) or near-optimal techniques that require iterative and uncertain tuning of evaluation parameters like the Pontryagin’s minimum principle (PMP). Recently, a novel near-optimal technique has been devised for rapidly predicting the optimal fuel economy benchmark of design options for electrified powertrains. This method, named slope-weighted energy-based rapid control analysis (SERCA), has been demonstrated producing results comparable to DP, while limiting the associated computational time by near two orders of magnitude.

The charge air cooler (CAC), which is placed between the compressor and the engine intake manifold (IM), is an important component in a turbocharged engine. It is essential to capture the temperature change, the pressure drop or the acoustical wave behavior of the charge air cooler in the one-dimensional(1D) simulation model for the predictive accuracy of engine performance and intake noise. In this paper, the emphasis is on the acoustic modeling of an intake manifold and charge air cooler assembly for the low frequency engine intake order noise. In this assembly, the core of the charge air cooler is embedded in the plenum of the intake manifold. The modeling and correlation process is comprised of three steps. First, the charge air cooler core is removed from the intake manifold and put into a rectangular box matching its envelope with a single air inlet and outlet, thereby simplifying the complex shape of the manifold with the different runner components.

This paper focuses on an inherent problems of active damping control prevalent in contemporary hybrid torque controls. Oftentimes, a supervisory torque controller utilizes simplified system models with minimal system states representation within the optimization problem, often not accounting for nonlinearities and stiffness. This is motivated by enabling the generation of the optimum torque commands with minimum computational burden. When inherent lash and stiffness of the driveline are not considered, the resulting command can lead to vibrations and oscillations in the powertrain, reducing performance and comfort. The paper proposes a Linear Quadratic Integral (LQI)-based compensator to be integrated downstream the torque supervisory algorithm, which role is to shape transient electric machine torques, compensating for the stiffness and backlash present in the vehicle while delivering the driver-requested wheel torque.

The trend to simultaneously improve fuel economy and engine performance has led to industry growth of turbocharged engines and as a result, the need to address their undesirable airborne noise attributes. This presents some unique engineering challenges as higher customer expectations for Noise Vibration Harshness (NVH), and other vehicle-level attributes increase over time. Turbocharged engines possess higher frequency noise content compared to naturally aspirated engines. Therefore, as an outcome, whoosh noise in the Air Induction System (AIS) during tip in conditions is an undesirable attribute that requires high frequency attenuation enablers. The traditional method for attenuation of this type of noise has been to use resonators which adds cost, weight and requires packaging space that is often at a premium in the under-hood environment.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

This paper presents a method to model the transmission mechanical power loss for the unloaded and loaded losses on a planetary gearset. In this analysis, the transmission losses are differentiated into losses due to fluid churning; losses due to fluid shear between the walls of rotating parts; losses due to fluid shear between motors' stator and rotor and losses due to the meshing of gearsets while transferring torque. This transmission mechanical power loss model is validated with test data that was obtained by independently testing an eVT transmission. The mechanical power loss model mentioned in this paper was constructed to accurately represent the test setup. From the correlation with the test data, it can be inferred that the transmission losses can be modeled within an error of 3% in the relevant region of output velocity for use in performance and fuel economy simulations.

Light-weight solutions for stamped steel components that exhibit the same or similar appearance properties for purposes of authentic feel and perception to customers will play a critical role as the progress towards reaching maximum fuel efficiency for large vehicles continues. This paper outlines the potential uses for laminated steel in large stamped steel bumper applications that would normally be stamped with thick sheet metal in order to meet vehicle level functional objectives. The paper presents the investigation of the one-for-one drop-in capabilities of the laminate steel material to existing stamping dies, special processing considerations while manufacturing, vehicle level performance comparisons, and class “A” coating options and process needs. Most of all, it will highlight the significant vehicle weight saving benefits and opportunities as compared to current production stamped steel bumpers.

The Hotelling multivariate control chart and the sample generalized variance |S| are used to monitor the mean and dispersion of vehicle build vision data including the pallet information to identify the non-conforming pallets that are used in body shops of FCA US LLC assembly plants. An iterative procedure and the Gaussian mixture model (GMM) are used to rank the non-conforming or bad pallets in the order of severity. The Hotelling multivariate T2 test statistic along with Mason-Tracy-Young (MYT) signal decomposition method is used to identify the features that are affected by the bad pallets. These algorithms were implemented in the Advanced Pallet Analysis module of the FCA US software Body Shop Analysis Toolbox (BSAT). The identified bad pallets are visualized in a scatter plot with a different color for each of the top bad pallets. The run chart of an affected feature confirms the bad pallet by highlighting data points from the bad pallet.

Cast aluminum alloys are used for cylinder heads in internal combustion engines to meet low weight and high strength (lightweight) design requirements. In the combustion chamber, the alloy experiences harsh operating conditions; i.e., temperature variation, constrained thermal expansion, chemical reaction, corrosion, oxidation, and chemical deposition. Under these conditions, thermomechanical fatigue (TMF) damage arises in the form of mechanical damage, environmental (oxidation) damage, and creep damage. In the present work, several important properties that influence the TMF life of the cylinder head have been identified through TMF and finite element analysis (FEA). The results show that improving the strength at high temperatures helps improve TMF life on the exhaust side of the head. On the other hand, improving strength and ductility extend TMF life at low temperature on the intake side.

Downsized turbocharged gasoline direct injection (TGDI) engines with high specific power and torque can enable reduced fuel consumption in passenger vehicles while maintaining or even improving on the performance of larger naturally aspirated engines. However, high specific torque levels, especially at low speeds, can lead to abnormal combustion phenomena such as knock or Low-Speed Pre-Ignition (LSPI). LSPI, in particular, can limit further downsizing due to resulting and potentially damaging mega-knock events. Herein, we characterize the impacts of lubricant and fuel composition on LSPI frequency in a TGDI engine while specifically exploring the correlation between fuel composition, particulate emissions, and LSPI events. Our research shows that: (1) oil composition has a strong impact on LSPI frequency and that LSPI frequency can be reduced through a carefully focused approach to lubricant formulation.

Two methods of assessing the similarity of a set of impact test signals have been proposed and used in the literature, which are cumulative variance-based and cross correlation-based. In this study, a normalized formulation unites these two approaches by establishing a relationship between the normalized cumulative variance metric (v), an overall similarity metric, and the normalized magnitude similarity metric (m) and shape similarity metric (s): v=1 − m · s. Each of these ranges between 0 and 1 (for the practical case of signals acquired with the same polarity), and they are independent of the physical unit of measurement. Under generally satisfied conditions, the magnitude similarity m is independent of the relative time shifts among the signals in the set; while the shape similarity s is a function of these.