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Instrument Panel Cluster (IPC), is a key ECU in vehicles. As IPC is a visual product, testing the software features of IPC is highly manual effort. Software Testing constitutes for approx. 35% of the total Software Development Life Cycle (SDLC). High focus on quick to market, shorter SDLC coupled with manual validation environment poses a challenge of increasing testing efficiency and improving software quality. This challenge drove the need to investigate a solution to automate the testing process and cut down the huge manual effort that goes into validating an Instrument Panel Cluster (IPC) software. The proposed intrusive and non-intrusive approaches to automate the testing process of IPC software employs a Frame Grabbing technique for the former approach and a Camera based technique for the latter. Both the approaches are robust, reliable, and scalable and covers the major portion of Vehicle Instrument cluster test scenarios.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

A methodology has been developed for identifying the combination of battery characteristics which lead to least-cost electric vehicles. Battery interrelationships include specific power vs, specific energy, peak power vs. specific energy and DOD, cycle life vs. DOD, cost vs. specific energy and peak power, and volumetric and battery size effects. The method is illustrated for the “second car” mission assuming lead/acid batteries. Reductions in life-cycle costs associated with future battery research breakthroughs are estimated using a sensitivity technique. A research prioritization system is described.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

The Modular Automotive Technology Testbed (MATT) is a flexible platform built to test different technology components in a vehicle environment. This testbed is composed of physical component modules, such as the engine and the transmission, and emulated components, such as the energy storage system and the traction motor. The instrumentation on the tool enables the energy balance for individual components on drive cycles. Using MATT, a single set of hardware can operate as a conventional vehicle, a hybrid vehicle and a plug-in hybrid vehicle, enabling direct comparison of petroleum displacement for the different modes. The engine provides measured fuel economy and emissions. The losses of components which vary with temperature are also measured.

Dry dual clutch transmission (DCT) has played an important role in the high performance applications as well as low-cost market sectors in Asia, with a potential as the future mainstream transmission technology due to its high mechanical efficiency and driving comfort. Control system simplification and cost reduction has been critical in making dry DCT more competitive against other transmission technologies. Specifically, DCT clutch actuation system is a key component with a great potential for cost-saving as well as performance improvement. In this paper, a new motor driven clutch actuator with a force-aid lever has been proposed. A spring is added to assist clutch apply that can effectively reduce the motor size and energy consumption. The goal of this paper is to investigate the feasibility of this new clutch actuator, and the force-aid lever actuator's principle, physical structure design, and validation results are discussed in details.

Fuel chemistry plays a crucial role in the continued reduction of particulate emissions (PE) and cleaner air quality from vehicles and equipment powered by internal combustion engines (ICE). Over the past ten years, there have been great improvements in predictive particulate emissions indices (correlative mathematical models) based on the fuel’s composition. Examples of these particulate indices (PI) are the Honda Particulate Matter Index (PMI) and the General Motors Particulate Evaluation Index (PEI). However, the analytical chemistry lab methods used to generate data for these two PI indices are very time-consuming. Because gasoline can be mixtures of hundreds of hydrocarbon compounds, these lab methods typically include the use of the high resolution chromatographic separation techniques such as detailed hydrocarbon analysis (DHA), with 100m chromatography columns and long (3 - 4 hours) analysis times per sample.

There are many challenges in implementing grid aware plug-in electric vehicle (PEV) charging systems with local load control. New opportunities for innovative load control were created as a result of changes to the 2014 National Electric Code (NEC) about automatic load control definitions for EV charging infrastructure. Stakeholders in optimized dispatch of EV charging assets include the end users (EV drivers), site owner/operators, facility managers and utilities. NEC definition changes allow for ‘over subscription’ of more potential EV charging station load than can be continuously supported if the total load at any time is within the supply system safety limit. Local load control can be implemented via compact submeter(s) with locally hosted control algorithms using direct communication to the managed electric vehicle supply equipment (EVSE).

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

Road vehicles can expend a significant amount of energy in undesirable vertical motions that are induced by road bumps, and much of that is dissipated in conventional shock absorbers as they dampen the vertical motions. Presented in this paper are some of the results of a study aimed at determining the effectiveness of efficiently transforming that energy into electrical power by using optimally designed regenerative electromagnetic shock absorbers. In turn, the electrical power can be used to recharge batteries or other efficient energy storage devices (e.g., flywheels) rather than be dissipated. The results of the study are encouraging - they suggest that a significant amount of the vertical motion energy can be recovered and stored.

Speaker performance in Acoustic Vehicle Alerting System (AVAS) plays a crucial role for pedestrian safety. Sound radiation from AVAS speaker has obvious directivity pattern. Considering this feature is critical for accurately simulating the exterior sound field of electrical vehicles. This paper proposes a new process to characterize the sound directivity pattern of AVAS speaker. The first step of the process is to perform an acoustic testing to measure the sound pressure radiated from the speaker at a certain number of microphone locations in a free field environment. Based on the geometry of a virtual speaker, the locations of each microphone and measured sound pressure data, an inverse method, namely the inverse pellicular analysis, is adopted to recover a set of vibration pattern of the virtual speaker surface. The recovered surface vibration pattern can then be incorporated in the full vehicle numerical model as an excitation for simulating the exterior sound field.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

The past five years have seen significant research into autonomous vehicles that employ a by-wire steering rack actuator and no steering wheel. There is a clear synergy between these advancements and the parallel development of complete Steer-by-Wire systems for human-operated passenger vehicle applications. Steer-by-Wire architectures presented thus far in the literature require multiple layers of electrical and/or mechanical redundancy to achieve the safety goals. Unfortunately, this level of redundancy makes it difficult to simultaneously achieve three key manufacturer imperatives: safety, reliability, and cost. Hindered by these challenges, as of 2020 only one production car platform employs a Steer-by-Wire system. This paper presents a Steer-by-Wire architectural solution featuring fail-operational steering control architected with the objective of achieving all system safety, reliability, and cost goals.

The Chevy Bolt’s One Pedal Driving feature is a new electrification propulsion enhancement that allows the driver to accelerate, decelerate and hold their vehicle stationary by just using the accelerator pedal. With this new feature, the driver is relieved of having to switch between pressing the accelerator pedal and brake pedal to slow, stop and hold the vehicle stationary. While this feature provides a convenience to the driver, it also presents a paradigm shift in driver engagement and control system responsibility for executing certain functions that the driver was traditionally responsible to perform. Various system safety techniques were involved in the development of such a feature both from a traditional functional safety perspective as well as a Safety of the Intended Functionality (SOTIF) perspective.

The automotive industry is facing many significant challenges that go far beyond the design and manufacturing of automobile products. Connected, autonomous and electric vehicles, smart cities, urbanization and the car sharing economy all present challenges in a fast-changing environment which the automotive industry must adapt to. Cars no longer are just standalone systems, but have become constituent systems (CS) in larger System of Systems (SoS) context. This is reflected in the emergence of several acronyms such as vehicle-to-everything (V2X), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-grid (V2G) expressions. System of Systems are defined systems of interest whose elements (constituent systems) are managerially and operationally independent systems. This interoperating and/or integrated collection of constituent systems usually produce results unachievable by the individual systems alone, for example the use of car batteries as virtual power plants.