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Impact of driving patterns on fuel economy is significant in hybrid electric vehicles (HEVs). Driving patterns affect propulsion and braking power requirement of vehicles, and they play an essential role in HEV design and control optimization. Driving pattern conscious adaptive strategy can lead to further fuel economy improvement under real-world driving. This paper proposes a real-time driving pattern recognition algorithm for supervisory control under real-world conditions. The proposed algorithm uses reference real-world driving patterns parameterized from a set of representative driving cycles. The reference cycle set consists of five synthetic representative cycles following the real-world driving distance distribution in the US Midwestern region. Then, statistical approaches are used to develop pattern recognition algorithm. Driving patterns are characterized with four parameters evaluated from the driving cycle velocity profiles.

In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

Electrochemical models of lithium ion batteries are today a standard tool in the automotive industry for activities related to the computer-aided engineering design, analysis, and optimization of energy storage systems for electrified vehicles. One of the challenges in the development or use of such models is the need of detailed information on the cell and electrode geometry or properties of the electrode and electrolyte materials, which are typically unavailable or difficult to retrieve by end-users. This forces engineers to resort to “hand-tuning” of many physical and geometrical parameters, using standard cell-level characterization tests. This paper proposes a method to provide information and data on individual electrode performance that can be used to simplify the calibration process for electrochemical models.

Designing a control system that can robustly detect faulted emission control devices under all environmental and driving conditions is a challenging task for OEMs. In order to gain confidence in the control strategy and the values of tunable parameters, the test vehicles need to be subjected to their limits during the development process. Complexity of modern powertrain systems along with the On-Board Diagnostic (OBD) monitors with multidimensional thresholds make it difficult to anticipate all the possible scenarios. Finding optimal solutions to these problems using traditional calibration processes can be time and resource intensive. A possible solution is to take a data driven calibration approach. In this method, a large amount of data is collected by collaboration of different groups working on the same powertrain. Later, the data is mined to find the optimum values of tunable parameters for the respective vehicle functions.

Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions).

The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

Automotive manufacturers continue to move further toward powertrain electrification. There are already many hybrid electric vehicles on the market that are based on a variety of system architectures. Ford Motor Company has investigated a new Dual Drive configuration that promises to overcome some of the attribute deficiencies associated with current architectures. The primary objective of this development project was to demonstrate the fuel economy potential of this system in a vehicle. To accomplish this objective, the team used an internally developed, formal Controls Development Process (CDP) for the control system design and validation. This paper describes the development of the vehicle control system in the context of this process.

With increasingly stringent emissions regulations and concurrent requirements for enhanced engine thermal efficiency, a comprehensive characterization of the automotive gasoline fuel spray has become essential. The acquisition of accurate and repeatable spray data is even more critical when a combustion strategy such as gasoline direct injection is to be utilized. Without industry-wide standardization of testing procedures, large variablilities have been experienced in attempts to verify the claimed spray performance values for the Sauter mean diameter, Dv90, tip penetration and cone angle of many types of fuel sprays. A new SAE Recommended Practice document, J2715, has been developed by the SAE Gasoline Fuel Injection Standards Committee (GFISC) and is now available for the measurement and characterization of the fuel sprays from both gasoline direct injection and port fuel injection injectors.

An advanced powertrain cooling system with appropriate control strategy and active actuators allows greater flexibility in managing engine temperatures and operating near constraints. An organized controls development process is necessary to allow comparison of multiple configurations to select the best way forward. In this work, we formulate, calibrate and validate a Model Predictive Controller (MPC) for temperature regulation and constraint handling in an advanced cooling system. A model-based development process was followed; where the system model was used to develop and calibrate a gain scheduled linear MPC. The implementation of MPC for continuous systems and the modification related to implementing switching systems has been described. Multiple hardware configurations were compared with their corresponding control system in simulations. The system level requirements were translated into MPC calibration parameters for consistent comparison between multiple configurations.

With the rapid development of artificial intelligence, the autonomous vehicles (AV) have attracted considerable attention in the automotive industry. However, different factors negatively impact the adoption of the AVs, delaying their successful commercialization. Accretion of atmospheric icing, especially wet snow, on AV sensors causes blockage on their lenses, making them prone to lose their sight, in turn, increasing potential chances of accidents. In this study, two different designs are proposed in order to prevent snow accretion on the lenses of AVs via air flow across the lens surface. In both designs, lenses made of plain glass and superhydrophobic coated glass surfaces are tested. While some researchers have shown promise of water repellency on superhydrophobic surfaces, more snow accretion is observed on the superhydrophobic surfaces, when compared to the plain glass lenses.

Automotive Product Development organisations are challenged with ever increasing levels of systems complexity driven by the introduction of new technologies to address environmental concerns and enhance customer satisfaction within a highly competitive and cost conscious market. The technical difficulty associated with the engineering of complex automotive systems is compounded by the increase in sophistication of the control systems needed to manage the integration of technology packages. Most automotive systems have an electro-mechanical structure with control and software features embedded within the system. The conventional methods for design analysis and synthesis are engineering discipline focused (mechanical, electrical, electronic, control, software).

This work discusses the development of SAE procedure J2747, “Hydraulic Pump Airborne Noise Bench Test”. This is a test procedure describing a standard method for measuring radiated sound power levels from hydraulic pumps of the type typically used in automotive power steering systems, though it can be extended for use with other types of pumps. This standard was developed by a committee of industry representatives from OEM's, suppliers and NVH testing firms familiar with NVH measurement requirements for automotive hydraulic pumps. Details of the test standard are discussed. The hardware configuration of the test bench and the configuration of the test article are described. Test conditions, data acquisition and post-processing specifics are also included. Contextual information regarding the reasoning and priorities applied by the development committee is provided to further explain the strengths, limitations and intended usage of the test procedure.

Globalization in the automotive industry has resulted in a tremendous competitive advantage to those companies who can internally communicate ideas and information effectively and in a timely manner. This paper discusses one such effort related to objectively testing vehicles for steering and handling characteristics by implementing standard test procedures, data acquisition hardware and analysis methods. Ford Motor Company's Vehicle Dynamics Test Section has refined a number of test procedures to the point that, with proper training, all design and development engineers can quickly acquire, analyze and share test results. Four of these procedures and output are discussed in detail.

This paper will describe the development of an in-vehicle data acquisition and analysis system. The problem facing the Vehicle Dynamics Test Section of Ford Motor Company was to replace an antiquated data recorder with a versatile in-vehicle data acquisition system capable of supporting vehicle dynamics testing and development. The following criteria for a system was developed: Quick and easy quick software and hardware setup Off-the-shelf hardware wherever possible User-friendly software Flexible Open-ended and modular design Rugged Cost effective Utilizing the above criteria a number of commercially available systems were evaluated and found to be lacking. Therefore it was decided that a system suitable for vehicle dynamics testing would have to be developed.

Ford Motor Company has developed a full hybrid electric vehicle with a power-split hybrid powertrain. There are constraints imposed by the high voltage system in such an HEV, that do not exist in conventional vehicles. A significant controls problem that was addressed in the Ford Escape and Mercury Mariner Hybrids was the determination of the desired powertrain operating point such that the vehicle attributes of fuel economy, performance and drivability are met, while satisfying these new constraints. This paper describes the control system that addressed this problem and the tests that were designed to verify its operation.

A four valve controller and electronic control circuits were developed to control the displacement of hydrostatic pump/motors (P/M's) utilized in an automobile with a hydrostatic transmission and hydropneumatic accumulator energy storage. Performance of the control system was evaluated. The controller uses four high-speed, two-way, single-stage poppet valves, functioning in the same manner as a 4-way, 3-position spool valve. Two such systems were used to control the displacement of two P/Ms, each system driving a front wheel of the vehicle. The valves were controlled electronically by a distributed-control dead-band circuit and valve driver boards. Testing showed that the control system's time response satisified driving demand needs, but that the control system's error was slightly larger than desired. This may lead to complications in some of the vehicle's operating modes.

While SAE double inverted flares have been in use for decades, leaking joints continue to be a problem for OEMs in production settings consuming time and energy to detect and correct them before releasing vehicles from the assembly plant. It should be noted that this issue is limited to first-time vehicle assembly; once a flared brake tube joint is sealed at the assembly plant it remains sealed during normal customer usage. From their inception through the late 1980s most brake tubes have been 3/16″ nominal diameter. With the advent of higher flow requirements of Traction Control and Yaw/Stability control systems, larger tubes of 1/4″ and 5/16″ size have also been introduced. While it was known that the first-time sealing capability of the 3/16″ joint was not 100%, leakers were generally containable in the production environment and the joint was regarded as robust.

The desire for improved vehicle fuel economy, driven by high gas prices and concerns over energy independence, have sparked interest and demand for hybrid electric vehicles. Hybrid electric vehicle propulsion systems exhibit complex interactions which need to be understood in order to maximize fuel economy over the range of operating modes. Model-based development processes which use vehicle system models capable of representing the functional behaviors with embedded controls are needed for fast, efficient design of vehicle control systems which manage overall energy usage. Model-based vehicle system development processes have been employed for a Dual Drive HEV system. The process for creating these vehicle system models is described along with an approach for using these models to develop HEV systems. Details of key subsystem models and the process for integration of full vehicle implementation level controls are discussed.

Brake system development and testing is spread over vehicle manufacturers, system and component suppliers. Test equipment from different sources, even resulting from different technology generations, different data analysis and report tools - comprising different and sometimes undocumented algorithms - lead to a difficult exchange and analysis of test results and, at the same time, contributes to unwanted test variability. Other studies regarding the test variability brought up that only a unified and unambiguous data format will allow a meaningful and comparative evaluation of these data and only standardization will reveal the actual reasons of test variability. The text at hand illustrates that a substantial part of test variability is caused by a misinterpretation of data and/or by the application of different algorithms.

In future vehicles (e.g. fuel cell vehicles, hybrid electric vehicles), the electrical system will have an important impact on the mechanical systems in the car (e.g. powertrain, steering). Furthermore, this coupling will become increasingly important over time. In order to develop effective designs and appropriate control systems for these systems, it is important that the plant models capture the detailed physical behavior in the system. This paper will describe models of two electrical components, a battery and a supercapacitor, which have been modeled in two ways: (i) modeling the plant and controller using block diagrams in Simulink and (ii) modeling the plant and controller in Dymola followed by compiling this model to an S-function for simulation in Simulink. Both the battery and supercapacitor model are based on impedance spectroscopy measurements and can be used for highly dynamic simulations.