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E-Mobility and low noise IC Engines has pushed product development teams to focus more on sound quality rather than just on reduced noise levels and legislative needs. Furthermore, qualification of products from a sound quality perspective from an end of line testing requirement is also a major challenge. End of line (EOL) NVH testing is key evaluation criteria for product quality with respect to NVH and warranty. Currently for subsystem or component level evaluation, subjective assessment of the components is done by a person to segregate OK and NOK components. As human factor is included, the process becomes very subjective and time consuming. Components with different acceptance criteria will be present and it’s difficult to point out the root cause for NOK components. In this paper, implementation of machine learning is done for acoustic source detection at end of line testing.

This research evaluates the capability of data-science models to classify the combustion events in Cooperative Fuel Research Engine (CFR) operated under Homogeneous Charge Compression Ignition (HCCI) conditions. A total of 10,395 experimental data from the CFR engine at the University of Michigan (UM), operated under different input conditions for 15 different fuel blends, were utilized for the study. The combustion events happening under HCCI conditions in the CFR engine are classified into four different modes depending on the combustion phasing and cyclic variability (COVimep). The classes are; no ignition/high COVimep, operable combustion, high MPRR, and early CA50. Two machine learning (ML) models, K-nearest neighbors (KNN) and Support Vector Machines (SVM), are compared for their classification capabilities of combustion events. Seven conditions are used as the input features for the ML models viz.

The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework for real-time control aimed at optimizing energy consumption with associated benefits. In this paper, we implement an optimal control framework, developed previously, in an Audi A3 etron plug-in hybrid electric vehicle, and demonstrate that we can improve the vehicle’s efficiency and travel time in a corridor including an on-ramp merging, a speed reduction zone, and a roundabout. Our exposition includes the development, integration, implementation and validation of the proposed framework in (1) simulation, (2) hardware-in-the-loop (HIL) testing, (3) connectivity enabled virtual reality based bench-test, and (4) field test in Mcity. We show that by adopting such inexpensive, yet effective process, we can efficiently integrate and test the control framework, establish proper connectivity and data transmission between different modules of the system, and reduce uncertainty.

This paper presents an experimental investigation of the impact of EGR dilution on the tradeoff between flame and end-gas autoignition heat release in a Spark-Assisted Compression Ignition (SACI) combustion engine. The mixture was maintained stoichiometric and fuel-to-charge equivalence ratio (ϕ′) was controlled by varying the EGR dilution level at constant engine speed. Under all conditions investigated, end-gas autoignition timing was maintained constant by modulating the mixture temperature and spark timing. Experiments at constant intake pressure and constant spark timing showed that as ϕ′ is increased, lower mixture temperatures are required to match end-gas autoignition timing. Higher ϕ′ mixtures exhibited faster initial flame burn rates, which were attributed to the higher laminar flame speeds immediately after spark timing and their effect on the overall turbulent burning velocity.

Non-reacting spray behaviors of the Engine Combustion Network “Spray G” gasoline fuel injector were investigated at flash and non-flash boiling conditions in an optically accessible single cylinder engine and a constant volume spray chamber. High-speed Mie-scattering imaging was used to determine transient liquid-phase spray penetration distances and observe general spray behaviors. The standardized “G2” and “G3” test conditions recommended by the Engine Combustion Network were matched in this work and the fuel was pure iso-octane. Results from the constant volume chamber represented the zero (stationary piston) engine speed condition and single cylinder engine speeds ranged from 300 to 2,000 RPM. As expected, the present results indicated the general spray behaviors differed significantly between the spray chamber and engine. The differences must be thoughtfully considered when applying spray chamber results to guide spray model development for engine applications.

High-pressure gasoline fuel injection is a means to improve combustion efficiency and lower engine-out emissions. The objective of this study was to quantify the effects of fuel injection pressure on transient gasoline fuel spray development for a wide range of injection pressures, including over 1000 bar, using a constant volume chamber and high-speed imaging. Reference grade gasoline was injected at fuel pressures of 300, 600, 900, 1200, and 1500 bar into the chamber, which was pressurized with nitrogen at 1, 5, 10, and 20 bar at room temperature (298 K). Bulk spray imaging data were used to quantify spray tip penetration distance, rate of spray tip penetration and spray cone angle. Near-nozzle data were used to evaluate the early spray development.

Low-pressure cooled EGR (LP-cEGR) systems can provide significant improvements in spark-ignition engine efficiency and knock resistance. However, open-loop control of these systems is challenging due to low pressure differentials and the presence of pulsating flow at the EGR valve. This research describes a control structure for Low-pressure cooled EGR systems using closed loop feedback control along with internal model control. A Smith Predictor based PID controller is utilized in combination with an intake oxygen sensor for feedback control of EGR fraction. Gas transport delays are considered as dead-time delays and a Smith Predictor is one of the conventional methods to address stability concerns of such systems. However, this approach requires a plant model of the air-path from the EGR valve to the sensor.

Automotive systems can generate un-intentional radio frequency energy. The levels of these emissions must be below maximum values set by the Original Equipment Manufacturer (OEM) for customer satisfaction and/or in order to meet governmental requirements. Due to the complexity of electromagnetic coupling mechanisms that can occur on a vehicle, many times it is difficult to measure and identify the noise source(s) without the use of an electromagnetic interference (EMI) receiver or spectrum analyzer (SA). An efficient and effective diagnostic solution can be to use a low-cost portable, battery powered RF detector with wide dynamic range as an alternative for automotive electromagnetic compatibility (EMC) and design engineers to identify, locate, and resolve radio frequency (RF) noise problems. A practical circuit described here can be implemented easily with little RF design knowledge, or experience.

This paper presents an overview of the evolution & revolution of automotive E/E architectures and how we at Bosch, envision the technology in the future. It provides information on the bottlenecks for current E/E architectures and drivers for their evolution. Functionalities such as automated driving, connectivity and cyber-security have gained increasing importance over the past few years. The importance of these functionalities will continue to grow as these cutting-edge technologies mature and market acceptance increases. Implementation of these functionalities in mainstream vehicles will demand a paradigm shift in E/E architectures with respect to in-vehicle communication networks, power networks, connectivity, safety and security. This paper expounds on these points at a system level.

Spark Assisted Compression Ignition (SACI) aims to increase the load limit of homogeneous charge compression ignition (HCCI) engines, enabling the benefits of dilute combustion over a larger engine operation range. Compared to HCCI, SACI exhibits higher cyclic variation of several combustion features. Due to the necessity of control of the timing of the auto-ignition event during SACI operation, a suitable characterization of the combustion at a given set of actuator inputs is required to enable robust model-based controls of combustion. This paper investigates statistical approaches to analyze in-cylinder pressure data of SACI in order to find a real or reconstructed cycle that will represent the important characteristics of combustion. To determine the representativeness of such a cycle, several combustion characteristics were compared that could serve as operational limits.

With the publication of ISO26262 [1] and the concept of Functional Safety, being able to identify the required safety integrity level for software components and defining the respective development steps has become increasingly important. A number of Tier 1 automotive suppliers, including Robert Bosch LLC, have been developing software for safety relevant systems, and have experience with a number of methods and tools for software analysis. This paper will focus on the pros and cons of the Criticality Analysis method. Criticality Analysis (CA) is a method that rates outputs, sub-components and inputs to a function based on the ASIL rating of the function. Faller [2] proposed the use of CA in conjunction with IEC 61508 safety standard, and this author proposes that the CA can also be used in conjunction with ISO 26262. CA allows taking a function with any ASIL rating and breaking down the signal chain to develop safety requirements at each stage (see [2, 3]).

This paper describes an investigation about the efficiency of safety-related non-functional software unit tests (NFSWUT). Well defined design, implementation and test processes are widely used in the respective industry. In order to fulfill the ISO 26262[1] requirement, additional effort is necessary to execute the NFSWUT. However, the efficiency of these tests is still not confirmed. This paper will provide an overview about an investigation of the effort-benefit ratio of the NFSWUT.

EPA and NHTSA have harmonized Regulations for Greenhouse Gas (GHG) emissions and Corporate Average Fuel Economy (CAFE) for model year (MY) 2012-16, published on April 1st, 2010. This requirement applies to all passenger cars and light trucks. Therefore the reduction of CO₂ emissions will be a major challenge for the automotive industry in the coming years to achieve the targets (GHG & CAFE) of 250 g/mi CO₂ and 35.5 miles per gallon (mpg) for MY 2016. In addition to combustion engine improvements, vehicle energy management and operating strategies offer a huge potential to reduce CO₂ emissions through innovative electronic systems. The paper will show a possible implementation of a holistic vehicle energy management system including the operating strategy "Free wheeling." Starting from a top-down approach, we have developed operating strategies that offer the possibility to optimize the energy usage of the entire system.

Active safety systems will have a great impact in the next generation of vehicles. This is partly originated by the increasing consumer's interest for safety and partly by new traffic safety laws. Control actions in the vehicle are based on an extensive environment model which contains information about relevant objects in vehicle surroundings. Sensor data fusion integrates measurements from different surround sensors into this environment model. In order to avoid system malfunctions, high reliability in the interpretation of the situation, and therefore in the environment model, is essential. Hence, the main idea of data fusion is to make use of the advantages of using multiple sensors and different technologies in order to fulfill these requirements, which are especially high due to autonomous interventions in vehicle dynamics (e. g. automatic emergency braking).

Flexible fuel vehicles (FFVs) are able to operate on a blend of ethanol and gasoline in any volumetric concentration of up to 85% ethanol (93% in Brazil). The estimation of ethanol content is crucial for optimized and robust performance in such vehicles. Even if an ethanol sensor is utilized, an estimation scheme independent of the ethanol sensor measurement retains advantages in enhancing the reliability of ethanol estimation and allowing on-board diagnostics. It is well-known that an exhaust gas oxygen (EGO) sensor could be utilized to estimate the ethanol content, which exploits the difference in stoichiometric air-to-fuel ratio (SAFR) between ethanol (9.0) and gasoline (14.6). The SAFR-based ethanol estimation has been shown to be prone to large errors with mass air flow sensor bias and/or fuel injector shift.

“Creep groan” is a braking systems noise that is observed when a vehicle is starting to move from a stopped condition with brake pressure applied. Motion takes place when brake pressure is reduced while a motive force, such as an idling engine through an automatic transmission, or gravity due to the vehicle being on a slope, is present. The vibration causing the sound is commonly thought to result from friction force variation in stick-slip mode. Detection and evaluation of “creep groan” noise has been a challenge for NVH test groups. First, this sound typically is not purely tonal like the more common brake squeal, although ultimately it may produce a tonal subjective impression. In this work the authors study different methods that may be applied to “creep groan” detection and evaluation.

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.

Robert Bosch LLC North America has developed and calibrated an engine management system for gasoline direct injection engines. This system controls the General Motors 3.6L DOHC 4 valve V6 engine which features direct injection, variable valve timing and electronic throttle control. This engine powers the 2008 model year Cadillac CTS and STS. It is the first GM production direct injection V6 engine in North America. It produces 304 HP at 6500 rpm and 370 Nm torque at 5200 rpm. Emissions meet LEV2 Bin5 standards. Interesting features include wall guided direct fuel injection, homogeneous split injection for fast catalyst light off and one of the industry's first isolated injection systems for noise reduction. This paper provides an overview of the features of this system and focuses on the calibration development.

We have developed an artificial neural network (ANN) based combustion model and have integrated it into a fluid mechanics code (KIVA3V) to produce a new analysis tool (titled KIVA3V-ANN) that can yield accurate HCCI predictions at very low computational cost. The neural network predicts ignition delay as a function of operating parameters (temperature, pressure, equivalence ratio and residual gas fraction). KIVA3V-ANN keeps track of the time history of the ignition delay during the engine cycle to evaluate the ignition integral and predict ignition for each computational cell. After a cell ignites, chemistry becomes active, and a two-step chemical kinetic mechanism predicts composition and heat generation in the ignited cells. KIVA3V-ANN has been validated by comparison with isooctane HCCI experiments in two different engines.