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The adoption of simulation is critical to reducing development time and enhancing system robustness for Advanced Driver Assistance Systems (ADAS). Automotive companies typically have an abundance of real data recorded from a vehicle which is suitable for open-loop simulations. However, recorded data is often not suitable to test closed-loop control systems since the recorded data cannot react to changes in vehicle movement. This paper introduces a methodology to create virtual driving scenarios from recorded vehicle data to enable closed-loop simulation. This methodology is applied to test a lane centering application. A lane centering application helps a driver control steering to stay in the current lane and control acceleration and braking to maintain a set speed or to follow a preceding vehicle. The driver’s vehicle is referred to as the ego vehicle. Other vehicles on the road are referred to as target vehicles.

In a hydraulic system, parameter variation, contamination, and/or operating conditions can lead to instabilities in the pressure response. The resultant erratic pressure profile reduces performance and can lead to hardware damage. Specifically, in a transmission control system, the inability to track pressure commands can result in clutch or variator slip which can cause driveline disturbance and/or hardware damage. A variator is highly sensitive to slip and therefore, it is advantageous to identify such pressure events quickly and take remedial actions. The challenge is to detect the condition in the least amount of time while minimizing false alarms. A Neyman-Pearson and an energy detector (based on auto-correlation) are evaluated for the detection of pressure disturbances. The performance of the detectors is measured in terms of speed of detection and robustness to measurement noise.

In this paper, a model-based linear estimator and a non-linear control law for an Fe-zeolite urea-selective catalytic reduction (SCR) catalyst for heavy duty diesel engine applications is presented. The novel aspect of this work is that the relevant species, NO, NO2 and NH3 are estimated and controlled independently. The ability to target NH3 slip is important not only to minimize urea consumption, but also to reduce this unregulated emission. Being able to discriminate between NO and NO2 is important for two reasons. First, recent Fe-zeolite catalyst studies suggest that NOx reduction is highly favored by the NO 2 based reactions. Second, NO2 is more toxic than NO to both the environment and human health. The estimator and control law are based on a 4-state model of the urea-SCR plant. A linearized version of the model is used for state estimation while the full nonlinear model is used for control design.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

It is estimated that operating continuously on a B20 fuel containing the current allowable ASTM specification limits for metal impurities in biodiesel could result in a doubling of ash exposure relative to lube-oil-derived ash. The purpose of this study was to determine if a fuel containing metals at the ASTM limits could cause adverse impacts on the performance and durability of diesel emission control systems. An accelerated durability test method was developed to determine the potential impact of these biodiesel impurities. The test program included engine testing with multiple DPF substrate types as well as DOC and SCR catalysts. The results showed no significant degradation in the thermo-mechanical properties of cordierite, aluminum titanate, or silicon carbide DPFs after exposure to 150,000 mile equivalent biodiesel ash and thermal aging. However, exposure of a cordierite DPF to 435,000 mile equivalent aging resulted in a 69% decrease in the thermal shock resistance parameter.

Model-based system simulations play a critical role in the development process of the automotive industry. They are highly instrumental in developing embedded control systems during conception, design, validation, and deployment stages. Whether for model-in-the-loop (MiL), software-in-the-loop (SiL) or hardware-in-the-loop (HiL) scenarios, high-fidelity plant models are particularly valuable for generating realistic simulation results that can parallel or substitute for costly and time-consuming vehicle field tests. In this paper, the development of a powertrain plant model and its correlation performance are presented. The focus is on the following modules of the propulsion systems: transmission, driveline, and vehicle. The physics and modeling approach of the modules is discussed, and the implementation is illustrated in Amesim software. The developed model shows good correlation performance against test data in dynamic events such as launch, tip-in, tip-out, and gearshifts.

In modern production diesel engine control systems, fuel path control is still largely conducted through a system of tables that set mode, timing and injection quantity and with common rail systems, rail pressure. In the hands of an experienced team, such systems have proved so far able to meet emissions standards, but they lack the analytical underpinning that lead to systematic solutions. In high degree of freedom systems typified by modern fuel injection, there is substantial scope to deploy optimising closed loop strategies during calibration and potentially in the delivered product. In an optimising controller, a digital algorithm will explicitly trade-off conflicting objectives and follow trajectories during transients that continue to meet a defined set of criteria. Such an optimising controller must be based on a model of the system behaviour which is used in real time to investigate the consequences of proposed control actions.

Smaller locomotives often use mechanical transmissions instead of diesel-electric drive systems typically used in larger locomotives. This paper discusses how Model Based Design was used to develop the complete drive train control system for a 24 ton sugar cane locomotive. A complete MATLAB Simulink machine model was built to fully test and verify the shift control logic, traction control, vehicle speed limiting, and braking control for this locomotive application before it was commissioned. The model included the engine, torque converter, planetary transmission, drive line, and steel on steel driving surface. Simulation was used to debug all control code and test and refine control strategies so that the initial field commissioning in remote Australia was executed very quickly with minimal engineering support required.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

A slipstream of exhaust from a Caterpillar 3126B engine was diverted into a plasma-catalytic NOx control system in the space velocity range of 7,000 to 100,000 hr-1. The stream was first fed through a non-thermal plasma that was formed in a coaxial cylinder dielectric barrier discharge reactor. Plasma treated gas was then passed over a catalyst bed held at constant temperature in the range of 573 to 773 K. Catalysts examined consisted of γ-alumina, indium-doped γ-alumina, and silver-doped γ-alumina. Road and rated load conditions resulted in engine out NOx levels of 250 - 600 ppm. The effects of hydrocarbon level, catalyst temperature, and space velocity are discussed where propene and in one case ultra-low sulfur diesel fuel (late cycle injection) were the reducing agents used for NOx reduction. Results showed NOx reduction in the range of 25 - 97% depending on engine operating conditions and management of the catalyst and slipstream conditions.

Currently, Model Based Development (MBD) is the de-facto methodology in automotive industry. This has led to conversions of legacy code to Simulink models. Our previous work was related to implementing the C2M tool to automatically convert legacy code to Simulink models. While the tool has been implemented and deployed on few OEM pilot code-sets there were several improvement areas identified w.r.t. the generated models. One of the improvement areas identified was that the generated model used atomic blocks instead of abstracted blocks available in Simulink. E.g. the generated model used an ADD block and feedback loop to represent an integration operation instead of using an integrator block directly. This reduced the readability of the model even though the functionality was correct. Thus, as a user of the model, an engineer would like to see abstract blocks rather than atomic blocks.

This paper presents a response to the question: Simulation - mathematical manipulation or useful design tool? A mathematical model of a modulating valve in a transmission control system was developed to predict clutch pressure modulation characteristics. The transmission control system was previously reported in SAE Paper 850783 - “Electronic/Hydraulic Transmission Control System for Off-Highway Vehicles”. The comparison of simulation predictions with test data illustrates the effectiveness of simulation as a design tool. THE EVOLUTION OF COMPUTER hardware and simulation software has resulted in increased interest and usage of simulation for dynamic analysis of hydraulic systems. Most commercially available software is relatively easy to learn to use. The application of such software and the modeling techniques involved require a longer learning curve.

This paper describes the design of a multivariable, constrained Model Predictive Control (MPC) system for torque tracking in turbocharged gasoline engines scheduled for production by General Motors starting in calendar year 2018. The control system has been conceived and co-developed by General Motors and ODYS. The control approach consists of a set of linear MPC controllers scheduled in real time based on engine operating conditions. For each MPC controller, a linear model is obtained by system identification with data collected from engines. The control system coordinates throttle, wastegate, intake and exhaust cams in real time to track a desired engine torque profile, based on measurements and estimates of engine torque and intake manifold pressure.

General Motors’ 3rd generation eAssist propulsion systems build upon the experience gained from the 2nd generation 115v system and the 1st generation 36v system. Extensive architectural studies were conducted to optimize the new eAssist system to maintain the performance and fuel economy gains of the 2nd generation 115v system while preserving passenger and cargo space, and reducing cost. Three diverse vehicle applications have been brought to production. They include two similar pickup trucks with 5.3 liter V8 engines and 8 speed transmissions, a 4-door passenger car with 2.5 liter 4 cylinder normally aspirated gasoline engine and a 6-speed automatic transmission, and a crossover SUV with a 2.0-liter turbocharged engine and 9 speed transmission. The key electrification components are a new water cooled induction motor/generator (MG), new water cooled power electronics module, and two major variants of 86v lithium ion battery packs.

As both the number and complexity of electronic control system applications on earthmoving equipment and on-highway trucks increase, so does the effort associated with developing and maintaining control strategies implemented in embedded systems. A new tool was recently introduced by Sigma Technology of Ann Arbor, Michigan, that provides the capability to perform rapid prototyping of production embedded systems. The rapid prototyping process includes system modeling, control algorithm synthesis, simulation analysis, source code generation and vehicle implementation. The results of incorporating this tool in the control system design process include improved control performance, improved system reliability/robustness, and significantly reduced development/maintenance costs.

In an earlier paper, the authors described how Model-Based System Engineering could be utilized to provide a virtual Hardware-in-the-Loop simulation capability, which creates a framework for the development of virtual ECU software by providing a platform upon which embedded control algorithms may be developed, tested, updated, and validated. The development of virtual ECU software is increasingly valuable in automotive control system engineering because vehicle systems are becoming more complex and tightly integrated, which requires that interactions between subsystems be evaluated during the design process. Variational analysis and robustness studies are also important and become more difficult to perform with real hardware as system complexity increases. The methodology described in this paper permits algorithm development to be performed prior to the availability of vehicle and control system hardware by providing what is essentially a virtual integration vehicle.

The purpose of this report is to describe the process for the development of the automatically shifting manual transmission control system hardware and software to be used in the MTU Challenge X Equinox, a through-the-road parallel hybrid electric vehicle. The automatically shifting manual transmission was chosen for development, as it combines the ease of use of an automatic transmission with the fuel efficiency of a manual, while eliminating the parasitic losses in the torque converter and the transmission hydraulic pump. This report illustrates the process used to develop the software-in-the loop modeling that was developed for the initial proof of concept. In addition, it describes the development of the control strategy and hardware build for the prototype transmission. To begin the design process research was preformed on existing automatically shifting manuals and manual transmissions in general. From there vehicle subsystems were assembled using Simulink block diagrams.

The hardware and software for a prototype computer controlled cooling system for a diesel powered truck has been designed and tested. The basic requirements for this system have been defined and the control functions, previously investigated in a study using the computer simulation model, were incorporated into the software. Engine dynamometer tests on the MACK-676 engine, comparing the conventional cooling system and the computer controlled system, showed the following advantages of the computer controlled system: 1. The temperature level to which the engine warms up to at low ambient temperature, was increased. 2. The faster shutter response reduced the temperature peaks and decreased total fan activity time. 3. The faster fan response reduces fan engagement time which should improve truck fuel economy.

Hardware-in-the-loop (HiL) simulation is an advanced technique for development and testing of complex real-time embedded systems. This technique has greatly developed in the last decades and has been more and more used in the automotive industry for algorithm and software development, hardware validation, safety validation, and fault investigation activities. Plant simulation model executes in HiL simulator to provide a virtual vehicle that interacts in an open-loop or closed-loop fashion with the embedded system that is under test. Compared to in-vehicle testing, HiL simulation provides benefits of low cost, high availability, high flexibility, repeatability, and test automation capability. HiL simulation reduces the risk caused by control failure, which is especially important for self-driving control system development and testing. The HiL simulation system is more application specific.

Adaptive cruise control is an advanced vehicle technology that is unique in its ability to govern vehicle behavior for extended periods of distance and time. As opposed to standard cruise control, adaptive cruise control can remain active through moderate to heavy traffic congestion, and can more effectively reduce greenhouse gas emissions. Its ability to reduce greenhouse gas emissions is derived primarily from two physical phenomena: platooning and controlled acceleration. Platooning refers to reductions in aerodynamic drag resulting from opportunistic following distances from the vehicle ahead, and controlled acceleration refers to the ability of adaptive cruise control to accelerate the vehicle in an energy efficient manner. This research calculates the measured greenhouse gas emissions benefit of adaptive cruise control on a fleet of 51 vehicles over 62 days and 199,300 miles.