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The present production General Motors 2-Mode Hybrid system for full-size SUVs and pickup trucks integrates truck utility functions with a full hybrid system. The 2-mode hybrid system incorporates two electro-mechanical power-split operating modes with four fixed-gear ratios. The combination provides fuel savings from electric assist, regenerative braking and low-speed electric vehicle operation. The combination of two power-split modes reduces the amount of mechanical power that is converted to electric power for continuously variable transmission operation, meeting the utility required for SUVs and trucks. This paper describes how fuel economy functionality was blended with full-size truck utility functions. Truck functions described include: Manual Range Select, Cruise Control, 4WD-Low and continuous high load operation.

As the automotive industry prepares to roll out an unprecedented range of fully electric propulsion vehicle models over the next few years - it really brings to a head for folks responsible for brakes what used to be the subject of hypothetical musings and are now pivotal questions for system design. How do we really go about designing brakes for electric vehicles, in particular, for the well-known limit condition of descending a steep grade? What is really an “optimal’ design for brakes considering the imperatives for the entire vehicle? What are the real “limit conditions” for usage that drive the fundamental design? Are there really electric charging stations planned for or even already existing in high elevations that can affect regenerative brake capacity on the way down? What should be communicated to drivers (if anything) about driving habits for electric vehicles in routes with significant elevation change?

The energy management strategies (EMS) for hybrid electric vehicles (HEV) have a great impact on the fuel economy (FE). The Pontryagin's minimum principle (PMP) has been proved to be a viable control strategy for HEV. The optimal costate of the PMP control can be determined by the given information of the driving conditions. Since the full knowledge of future driving conditions is not available, this paper proposed a dynamic optimization method for PMP costate without the prediction of the driving cycle. It is known that the lower fuel consumption the method yields, the more efficiently the engine works. The selection of costate is designed to make the engine work in the high efficiency range. Compared with the rule-based control, the proposed method by the principle of Hamiltonian, can make engine working points have more opportunities locating in the middle of high efficiency range, instead of on the boundary of high efficiency range.

The vehicle chassis integrated control system can improve the stability of vehicles under extreme conditions using tire force allocation algorithm, in which, the nonlinearity and uncertainty of tire-road contact condition need to be taken into consideration. Thus, An MPC (Model Predictive Control) controller is designed to obtain the additional steering angle and the additional yaw moment. By using a robust optimal allocation algorithm, the additional yaw moment is allocated to the slip ratios of four wheels. An SMC (Sliding-Mode Control) controller is designed to maintain the desired slip ratio of each wheel. Finally, the control performance is verified in MATLAB-CarSim co-simulation environment with open-loop manoeuvers.

EMC Component Validation Responsibilities encompass many realms. One of these realms is the effect of magnetic fields on silicon-based devices. This article describes a method for exposing these devices to magnetic fields with waveforms other than the traditional sinusoidal excitation. The method commonly used to explore the sensitivity of active silicon devices is exposure of the device to a representative sinusoidal field and observation of its reaction or lack thereof. The challenge is to characterize the representative field and be able to verify its effectiveness. Recent vehicle level testing of new designs has brought our attention to time-varying or transient magnetic field shapes that create deviations not previously detected with Military Standard 461 (MIL-STD-461) type sinusoidal magnetic field exposure.

In the current work, unidirectional (UD) carbon fiber composite hatsection component with two different layups are studied under dynamic three-point bending loading. The experiments are performed at various impact velocities, and the effects of impactor velocity and layup on acceleration histories are compared. A macro model is established with LS-DYNA for a more detailed study. The simulation results show that the delamination plays an important role during dynamic three-point bending test. Based on the analysis with a high-speed camera, the sidewall of hatsection shows significant buckling rather than failure. Without considering the delamination, the current material model cannot capture the post-failure phenomenon correctly. The sidewall delamination is modeled by assumption of larger failure strain together with slim parameters, and the simulation results of different impact velocities and layups match the experimental results reasonably well.

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.

Modern hybrid technologies, especially mild and micro-hybrids with auto start/stop feature, demand a starter with higher power, better performance and longer life than conventional brush-type starters. In this paper, a new starter design using a brushless motor is proposed. This improves the engine crank performance during autostarts due to lower inertia, higher torque and wider power band capability of the brushless motor, especially at higher speeds. The overall integrated system includes the motor, inverter and controller all packaged in the same form factor of the original starter housing as a “drop-in replacement”. The prototype starter motor is designed to operate at 48V with a peak power of 4kW but can be designed to operate at the standard 12V. This paper will describe in detail the functionalities of the overall system and the simulation and experimental results of the prototype that was tested on a 4-cylinder engine in a production crossover vehicle.

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.

Flash boiling is considered a useful method in enhancing the liquid fuel jet break-up and spray atomization process for internal combustion engine applications. Spray atomization efficiency plays a vital role in the combustion process. Although some researches have demonstrated that flash boiling has the potential to improve the combustion efficiency and optimize emission-related issues, the effect of flash boiling spray characteristics on the combustion process has not been fully investigated. In this paper, spray characteristics and its related combustion process were studied via various non-intrusive diagnostics methods. The spray and combustion process under different test conditions were studied using an optical engine. It was found that by using flash boiling atomization, the combustion duration was reduced and IMEP enhanced significantly. Experimental results have built the relationship between flash boiling spray characteristics and the combustion performance in the engine.

Reliable driveaway from frozen condition is one of the challenging design and control problem for fuel cell applications. Different approaches for warmup from frozen conditions have been developed by OEMs, e.g. low voltage inefficient operation, or use of coolant heaters. However, most methods result in water generation which risk icing and blocking the valves and rendering them nonfunctional till they thaw. One such valve is the anode drain valve which is needed to remove water that crosses over across the membrane to anode side. This work discusses characterization of dynamics of water crossover to anode balance of plant via step response experiments on full scale systems, and development of an online estimator to detect onset of anode water crossover via this online observer. In addition, detection via voltage dip-based feedback is also presented.

An investigation of high speed direct injection (DI) compression ignition (CI) engine combustion fueled with gasoline (termed GDICI for Gasoline Direct-Injection Compression Ignition) in the low temperature combustion (LTC) regime is presented. As an aid to plan engine experiments at full load (16 bar IMEP, 2500 rev/min), exploration of operating conditions was first performed numerically employing a multi-dimensional CFD code, KIVA-ERC-Chemkin, that features improved sub-models and the Chemkin library. The oxidation chemistry of the fuel was calculated using a reduced mechanism for primary reference fuel combustion. Operation ranges of a light-duty diesel engine operating with GDICI combustion with constraints of combustion efficiency, noise level (pressure rise rate) and emissions were identified as functions of injection timings, exhaust gas recirculation rate and the fuel split ratio of double-pulse injections.

Unusual noises during vehicle acceleration often reflect poorly on customer perception of product quality and must be removed in the product development process. Flow simulation can be a valuable tool in identifying root causes of exhaust noises created due to tailpipe openings surrounded by fascia structure. This paper describes a case study where an unsteady Computational Fluid Dynamics (CFD) simulation of the combined flow and acoustic radiation from an exhaust opening through fascia components provided valuable insight into the cause of an annoying flow noise. Simulation results from a coupled thermal/acoustic analysis of detailed tailpipe opening geometry were first validated with off-axis microphone spectra under wide open throttle acceleration. After studying the visualizations of unsteady flow velocity and pressure from the CFD, a problem that had proved difficult to solve by traditional “cut and try” methods was corrected rapidly.

The diesel particulate filters (DPF) are considered the most robust technologies for particle emission reduction both in terms of mass and number. On the other hand, the increase of the backpressure in the exhaust system due to the accumulation of the particles in the filter walls leads to an increase of the engine fuel consumption and engine power reduction. To limit the filter loading, and the backpressure, a periodical regeneration is needed. Because of the growing interest about particle emission both in terms of mass, number and size, it appears important to monitor the evolution of the particle mass and number concentrations and size distribution during the regeneration of the DPFs. For this matter, in the presented work the regeneration of a catalyzed filter was fully analyzed. Particular attention was dedicated to the dynamic evolution both of the thermodynamic parameters and particle emissions.

The present paper describes the results of a cooperative research project between GM Powertrain Europe and Istituto Motori - CNR aimed at studying the capability of GM Combustion Closed-Loop Control (CLCC) in enabling seamless operation with high biodiesel blending levels in a modern diesel engine for passenger car applications. As a matter of fact, fuelling modern electronically-controlled diesel engines with high blends of biodiesel leads to a performance reduction of about 12-15% at rated power and up to 30% in the low-end torque, while increasing significantly the engine-out NOx emissions. These effects are both due to the interaction of the biodiesel properties with the control logic of the electronic control unit, which is calibrated for diesel operation. However, as the authors previously demonstrated, if engine calibration is re-tuned for biodiesel fuelling, the above mentioned drawbacks can be compensated and the biodiesel environmental inner qualities can be fully deployed.

Road induced structural feel “vehicle feels solidly built” is strongly related to the vehicle ride [1]. Excellent structural feel requires both structural and suspension dynamics considerations simultaneously. Road induced structural feel is defined as customer facing structural and component responses due to tire force inputs stemming from the unevenness of the road surface. The customer interface acceleration and noise responses can be parsed into performance criteria to provide design and tuning vehicle integration program recommendations. A dynamic impact bump method is developed for vehicle level structural feel performance assessment, diagnostics, and development tuning. Current state of on-road testing has the complexity of multiple impacts, averaging multiple road induced tire patch impacts over a length of a road segment, and test repeatability challenges.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

In light-duty direct-injection (DI) diesel engines, combustion chamber geometry influences the complex interactions between swirl and squish flows, spray-wall interactions, as well as late-cycle mixing. Because of these interactions, piston bowl geometry significantly affects fuel efficiency and emissions behavior. However, due to lack of reliable in-cylinder measurements, the mechanisms responsible for piston-induced changes in engine behavior are not well understood. Non-intrusive, in situ optical measurement techniques are necessary to provide a deeper understanding of the piston geometry effect on in-cylinder processes and to assist in the development of predictive engine simulation models. This study compares two substantially different piston bowls with geometries representative of existing technology: a conventional re-entrant bowl and a stepped-lip bowl. Both pistons are tested in a single-cylinder optical diesel engine under identical boundary conditions.

Stringent particulate emission regulations are applied to spark-ignition direct-injection (SIDI) engines, calling for a significant in-cylinder reduction of soot particles. To enhance fundamental knowledge of the soot formation and oxidation process inside the cylinder of the engine, a new in-flame particle sampling system has been developed and implemented in a working optical SIDI engine with a side-mounted, wall-guided injection system. Using the sampling probes installed on the piston top, the soot particles are directly sampled from the petrol flame for detailed analysis of particle size distribution, structure, and shape. At the probe tip, a transmission electron microscope (TEM) grid is stored for the soot collection via thermophoresis, which is imaged and post-processed for statistical analysis. Simultaneously, the flame development was recorded using two high-speed cameras to evidence the direct exposure of the sampling grids to the soot-laden diffusion flames and pool fires.

More than four decades ago, the concept of zero defects was coined by Phillip Crosby. It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO). Detecting these rare quality events is one of the modern intellectual challenges posed to this industry. Process Monitoring for Quality (PMQ) is an AI and big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right Machine Learning (ML), optimization, and statistics techniques are applied to develop an effective predictive system.