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A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

Downsizing and boosting is currently the principal solution to reduce fuel consumption in automotive engines without penalizing the power output. A key challenge for controlling the boost pressure during highly transient operations lies in avoiding to operate the turbocharger compressor in its instability region, also known as surge. While this phenomenon is well known by control engineers, it is still difficult to accurately predict during transient operations. For this reason, the scientific community has directed considerable efforts to understand the phenomena leading to the onset of unstable behavior, principally through experimental investigations or high-fidelity CFD simulations. On the other hand, less emphasis has been placed on creating control-oriented models that adopt a physics-based (rather than data-driven) approach to predict the onset of instability phenomena.

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances.

Downsizing and turbocharging are today considered an effective way to reduce CO2 emissions in automotive gasoline engines, especially for the European and US markets. In the broad field of research and development for engine boosting systems, the instability phenomenon of surge has gathered considerable interest in recent years, as the main limiting factor to high performance boosting and boost pressure control. To this extent, developing an in-depth knowledge of the surge dynamics and on the phenomena governing the transition from stable to unstable operation can provide very valuable information for the design of the intake system and boost pressure control algorithms, allowing optimal boost pressure without compromising the transient response.

A quasi-dimensional model for a direct injection diesel engine was developed based on experiments at Sandia National Laboratory. The Sandia researchers obtained images describing diesel spray evolution, spray mixing, premixed combustion, mixing controlled combustion, soot formation, and NOx formation. Dec [1] combined all of the available images to develop a conceptual diesel combustion model to describe diesel combustion from the start of injection up to the quasi-steady form of the jet. The end of injection behavior was left undescribed in this conceptual model because no clear image was available due to the chaotic behavior of diesel combustion. A conceptual end-of-injection diesel combustion behavior model was developed to capture diesel combustion throughout its life span. The compression, expansion, and gas exchange stages are modeled via zero-dimensional single zone calculations.

To improve torque management algorithms for drivability, the powertrain controller must be able to compensate for the nonlinear dynamics of the driveline. In particular, the presence of backlash in the transmission and drive shafts excites sharp torque fluctuations during tip-in or tip-out transients, leading to a deterioration of the vehicle drivability and NVH. This paper proposes a model-based estimator that predicts the wheel torque in an automotive drivetrain, accounting for the effects of backlash and drive shaft flexibility. The starting point of this work is a control-oriented model of the transmission and vehicle drivetrain dynamics that predicts the wheel torque during tip-in and tip-out transients at fixed gear. The estimator is based upon a switching structure that combines a Kalman Filter and an open-loop prediction based on the developed model.

In a conventional passenger vehicle, the AC system is the largest ancillary load. This paper proposes a novel control strategy to reduce the energy consumption of the air conditioning system of a conventional passenger car. The problem of reducing the parasitic load of the AC system is first approached as a multi-objective optimization problem. Starting from a validated control-oriented model of an automotive AC system, an optimization problem is formalized to achieve the best possible fuel economy over a regulatory driving cycle, while guaranteeing the passenger comfort in terms of cabin temperature and reduce the wear of the components. To complete the formulation of the problem, a set of constraints on the pressure in the heat exchanger are defined to guarantee the safe operation of the system. The Dynamic Programming (DP), a numerical optimization technique, is then used to obtain the optimal solution in form of a control sequence over a prescribed driving cycle.

Two modern light-duty passenger vehicles were selected for chassis dynamometer testing to evaluate differences in performance end efficiency resulting from CNG and gasoline combustion in a vehicle-based context. The vehicles were chosen to be as similar as possible apart from fuel type, sharing similar test weights and identical driveline configurations. Both vehicles were tested over several chassis dynamometer driving cycles, where it was found that the CNG vehicle exhibited 3-9% lower fuel economy than the gasoline-fueled subject. Performance tests were also conducted, where the CNG vehicle's lower tractive effort capability and longer acceleration times were consistent with the lower rated torque and power of its engine as compared to the gasoline model. The vehicles were also tested using quasi-steady-state chassis dynamometer techniques, wherein a series of engine operating points were studied.

The automotive industry is striving to adopt model-based engine design and optimization procedures to reduce development time and costs. In this scenario, first-principles gas dynamic models predicting the mass, energy and momentum transport in the engine air path system with high accuracy and low computation effort are extremely important today for performance prediction, optimization and cylinder charge estimation and control. This paper presents a comparative study of two different modeling approaches to predict the one-dimensional unsteady compressible flow in the engine air path system. The first approach is based on a quasi-3D finite volume method, which relies on a geometrical reconstruction of the calculation domain using networks of zero-dimensional elements. The second approach is based on a model-order reduction procedure that projects the nonlinear hyperbolic partial differential equations describing the 1D unsteady flow in engine manifolds onto a predefined basis.

The first commercially available Plug-In Hybrid Electric Vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in December 2010. The Volt's powertrain architecture provides four modes of operation, including two that are unique and maximize the Volt's efficiency and performance. The electric transaxle has been specially designed to enable patented operating modes both to improve the electric driving range when operating as a battery electric vehicle and to reduce fuel consumption when extending the range by operating with an internal combustion engine (ICE). However, details on the vehicle control strategy are not widely available because the supervisory control algorithm is proprietary. Since it is not possible to analyze the control without vehicle test data obtained from a well-designed Design-of-Experiment (DoE), a highly instrumented GM Volt, including thermal sensors, was tested at Argonne National Laboratory's Advanced Powertrain Research Facility (APRF).

Global fuel economy and CO2 reduction mandates are driving the need for a substantial increase in vehicle fuel efficiency over the next several years, with improvements coming from many sources. This paper describes a vehicle demonstration program to improve fuel economy by implementing a systems approach to reduce friction and parasitic losses. The work concentrated on nearer term technologies that can be quickly transferred to production vehicle programs. Major technologies demonstrated on the vehicle included gasoline direct injection (GDi) with cooled EGR, advanced valvetrain, rollerization of both crankshaft and camshaft, and stop-start engine operation. The work described in this paper comprises Phase I of a two phase program funded by DOE contract DE-EE0003258. It includes a hardware overview and a description of the system development activities. Results focus on vehicle fuel economy benefits compared to a production baseline vehicle.

In the last few years, the application of downsizing and turbocharging to internal combustion engines has considerably increased due to the proven potential of this technology to increase engine efficiency. Variable geometry turbines have been largely adopted to optimize the exhaust energy recovery over a large operating range. Two-stage turbocharger systems have also been studied as a solution to improve engine low-end torque and efficiency, with the first units currently available on the market. However, the compressor technology is today still based on fixed geometry machines, which are sized to efficiently operate at the maximum air flow and therefore lead to poor efficiency values at low air flow conditions. Furthermore, the surge limits prevents the full capabilities of VGT systems to increase the boosting at low engine speed.

In order to increase the efficiency of automotive turbochargers at low speed without compromising the performance at maximum boost conditions, variable geometry compressor (VGC) systems, based on either variable inlet guide vanes or variable geometry diffusers, have been recently considered as a future design option for automotive turbochargers. This work presents a modeling, analysis and optimization study for a Diesel engine equipped with a variable geometry compressor that help understand the potentials of such technology and develop control algorithms for the VGC systems,. A cycle-averaged engine system model, validated on experimental data, is used to predict the most important variables characterizing the intake and exhaust systems (i.e., mass flow rates, pressures, temperatures) and engine performance (i.e., torque, BMEP, volumetric efficiency), in steady-state and transient conditions.

EmiSense Technologies, LLC (www.emisense.com) is commercializing its electronic particulate matter (PM) sensor that is based on technology developed at the University of Texas at Austin (UT). To demonstrate the capability of this sensor for real-time PM measurements and on board diagnostics (OBD) for failure detection of diesel particle filters (DPF), independent measurements were performed to characterize the engine PM emissions and to compare with the PM sensor response. Computational fluid dynamics (CFD) modeling was performed to characterize the hydrodynamics of the sensor's housing and to develop an improved PM sensor housing with reproducible hydrodynamics and an internal baffle to minimize orientation effects. PM sensors with the improved housing were evaluated in the truck exhaust of a heavy duty (HD) diesel engine tested on-road and on a chassis dynamometer at the University of California, Riverside (UCR) using their Mobile Emissions Laboratory (MEL).

Two-stage turbochargers are a recent solution to improve engine performance, reducing the turbo-lag phenomenon and improving the matching. However, the definition of the control system is particularly complex, as the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization. This work documents a characterization study of two-stage turbocharger systems. The study relies on a mean-value model of a Diesel engine equipped with a two-stage turbocharger, validated on experimental data. The turbocharger is characterized by a VGT actuator and a bypass valve (BPV), both located on the high-pressure turbine. This model structure is representative of a “virtual engine”, which can be effectively utilized for applications related to analysis and control. Using this tool, a complete characterization was conducted considering key operating conditions representative of FTP driving cycle operations.

Two-stage turbochargers are a recent solution to improve engine performance. The large flexibility of these systems, able to operate in different modes, can determine a reduction of the turbo-lag phenomenon and improve the engine tuning. However, the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization to maximize the benefits of this technology. In addition, the design and calibration of the control system is particularly complex. The transitioning between single stage and two-stage operations poses further control issues. In this scenario a model-based approach could be a convenient and effective solution to investigate optimization, calibration and control issues, provided the developed models retain high accuracy, limited calibration effort and the ability to run in real time.

This paper describes a diesel engine lean NOx trap (LNT) regeneration air to fuel ratio (AFR) control system using a nonlinear model predictive control (NMPC) technique for simultaneous regeneration fuel penalty and overall tailpipe-out NOx reductions. A physics-based and experimentally validated nonlinear LNT dynamic model was employed to construct the NMPC control algorithm, which dictates the AFR value during regenerations. Different choices of NMPC cost function were examined in terms of the impact on fuel penalty and total tailpipe NOx slip amount. The cost function to achieve the best tradeoff between fuel penalty and tailpipe-out NOx was selected based on physical insights into the LNT system NOx and oxygen storage dynamics. The NMPC regeneration AFR control system was evaluated on a vehicle simulator cX-Emissions1 with a 1.9L diesel engine model through the FTP75 driving cycle.

An electronic particulate matter sensor (EPMS) developed at the University of Texas was used to characterize exhaust gases from a single-cylinder diesel engine and a light-duty diesel vehicle. Measurements were made during transient tip-in events with multiple sensor configurations in the single-cylinder engine. The sensor was operated in two modes: one with the electric field energized, and the other with no electric field present. In each mode, different characteristic signals were produced in response to a tip-in event, highlighting the two primary mechanisms of sensor operation. The sensor responded to both the natural charge of the particulate matter (PM) emitted from the engine, and was also found to create a signal by charging neutral particles. The characteristics of the two mechanisms of operation are discussed as well as their implications on the placement and operation of the sensor.

This paper presents the latest developments in the design and performance of an electronic particulate matter (PM) sensor developed at The University of Texas at Austin (UT) and suitable, with further development, for applications in active engine control of PM emissions. The sensor detects the carbonaceous mass component of PM in the exhaust and has a time-resolution less than 20 (ms), allowing PM levels to be quantified for engine transients. Sample measurements made with the sensor in the exhaust of a single-cylinder light duty diesel engine are presented for both steady-state and transient operations: a steady-state correlation with gravimetric filter measurements is presented, and the sensor response to rapid increases in PM emission during engine transients is shown for several different tip-in (momentary increases in fuel delivery) conditions.

Homogeneous Charge Compression Ignition (HCCI) is considered a very promising concept to achieve low NOx and Particulate Matter emissions in traditional spark ignition and Diesel engines. However, controlling the complex mechanisms which govern the combustion process and finding a proper method for the fuel introduction for Diesel HCCI engines have proven to still be a challenge. In addition, the well known IMEP limitations of HCCI combustion restrict the benefits on emissions to low engine load conditions. The current work attempts to extend the benefits of HCCI combustion to a broader range of engine operating conditions by blending the conventional Direct Injection (DI) with the external fuel atomization. A dual combustion system could potentially overcome the limits of low-load operations and allow for a gradual transition between the conventional DI mode at high load and the HCCI external mixture formation at idle and low load.