Search Results

Electrification of vehicles is an important step towards making mobility more sustainable and carbon-free. Hybrid electric vehicles use an electric machine with an on-board energy storage system, in some form to provide additional torque and reduce the power requirement from the internal combustion engine. It is important to control and optimize this power source split between the engine and electric machine to make the best use of the system. This paper showcases an implementation of the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with minimization in real-time in the dSPACE MicroAutobox II as the Hybrid Supervisory Controller (HSC). While the concept of A-ECMS has been well established for many years, there are no published papers that present results obtained in a production vehicle suitably modified from conventional to hybrid electric propulsion including real world testing as well as testing on regulatory cycles.

NASA’s Watts on the Moon Challenge is seeking solutions to transfer at least 1.065 kW power from a 120 V dc source to a 24-32 V dc load over a 3-km distance under the same environmental conditions as the Lunar surface (i.e., 77 K temperature and 1 mTorr pressure). The selected solution from the author’s team proposed utilizing two modular multilevel Gallium Nitride (GaN) based isolated dc-dc converters to connect the 120 V dc source with the 24-32 V dc load bank via 1.5 kV rated dc transmission lines. The modular multilevel converters feature frequency multiplication, high step-down voltage ratio and low device voltage stress. In the converters, GaN gate injection transistor (GaN GIT) and GaN High-Electron-Mobility Transistor (GaN HEMT) devices are chosen as switching devices, due to the merits of lower power loss, radiation hardness and ability to work under cryogenic and vacuum conditions.

Conventional silencers have extensively been used to attenuate airborne pressure pulsations in the breathing system of internal combustion engines, typically at low frequencies as dictated by the crankshaft speed. With the introduction of turbocharger compressors, however, particularly those with the ported shroud recirculating casing treatment, high-frequency tones on the order of 10 kHz have become a significant contributor to noise in the induction system. The elevated frequencies promote multi-dimensional wave propagation, rendering traditional silencing design methods invalid, as well as the standard techniques to assess silencer performance. The present study features a novel high-frequency silencer designed to target blade-pass frequency (BPF) noise at the inlet of turbocharger compressors. The concept uses an acoustic straightener to promote planar wave propagation across arrays of quarter-wave resonators, achieving a broadband attenuation.

Backlash is the movement between the gear teeth that allows them to mate without binding. Backlash can cause large torque fluctuations in vehicle powertrains when the input torque changes direction. These fluctuations cause a jerk and shuddering, which negatively affects drive quality. Input torque frequently changes direction in electric vehicles due to regenerative braking. Limiting zero crossings is an option for better drive quality; however, this leads to decreased vehicle efficiency. Because of this, modulating the torque through the backlash region is preferred, yet, if done poorly, it can result in sluggish torque response. This paper proposes a torque-shaping algorithm for an electric motor and gear/differential system to reduce backlash in electric vehicles. The control algorithm modulates the commanded torque’s rate of change based on the vehicle speed and zero-crossing torque.

1 Autonomous Driving Systems (ADS) are developing rapidly. As vehicle technology advances to SAE level 3 and above (L4, L5), there is a need to maximize and verify safety and operational benefits. As a result, maintenance of these ADS systems is essential which includes scheduled, condition-based, risk-based, and predictive maintenance. A lot of techniques and methods have been developed and are being used in the maintenance of conventional vehicles as well as other industries, but ADS is new technology and several of these maintenance types are still being developed as well as adapted for ADS. In this work, we are presenting a systematic literature review of the “State of the Art” knowledge for the maintenance of a fleet of ADS which includes fault diagnostics, prognostics, predictive maintenance, and preventive maintenance.

For realistic traffic modeling, real-world traffic calibration data is needed. These data include a representative road network, road users count by type, traffic lights information, infrastructure, etc. In most cases, this data is not readily available due to cost, time, and confidentiality constraints. Some open-source data are accessible and provide this information for specific geographical locations, however, it is often insufficient for realistic calibration. Moreover, the publicly available data may have errors, for example, the Open Street Maps (OSM) does not always correlate with physical roads. The scarcity, incompleteness, and inaccuracies of the data pose challenges to the realistic calibration of traffic models. Hence, in this study, we propose an approach based on spatial interpolation for addressing sparsity in vehicle count data that can augment existing data to make traffic model calibrations more accurate.

In the last decade, the use of Driver-in-the-Loop (DiL) simulators has significantly increased in research, product development, and motorsports. To be used as a verification tool in research, simulators must show a level of correlation with real-world driving for the chosen use case. This study aims to assess the validity of a low-cost, limited travel Vehicle Dynamics Driver-in-Loop (VDDiL) simulator by comparing on-road and simulated driving data using a statistical evaluation of longitudinal and lateral metrics. The process determines if the simulator is appropriate for verifying control strategies and optimization algorithms for longitudinal vehicle dynamics and evaluates consistency in the chosen metrics. A validation process explaining the experiments, choice of metrics, and analysis tools used to perform a validation study from the perspective of the longitudinal vehicle model is shown in this study.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

Second-life batteries (SLBs, automotive batteries that have lost their usefulness for vehicular applications) can provide low-cost environment-friendly solutions for grid-connected systems. The estimation of the remaining useful life (RUL) of SLBs is a fundamental step for the development of appropriate business models. This paper aims at unveiling correlations between the SLB's power profile and aging performance by defining appropriate metrics. A widely accepted empirical degradation model, that can predict calendar and cycling aging, is considered for this study. Several grid-connected power profiles are analyzed, such as peak shaving for DC-fast charge stations and frequency regulation. The results of this analysis show a correlation between the SLB's replacement rate with the minimum daily SoC.

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping.

Simulation-based design of connected and automated hybrid-electric vehicles is a challenging problem. The design space is large, the systems are complex, and the influence of connected and autonomous technology on the process is a new area of research. The Ohio State University EcoCAR Mobility Challenge team developed a comprehensive design and simulation approach as a solution. This paper covers the detailed simulation work conducted after initial design space reduction was performed to arrive at a P0-P4 hybrid vehicle with a gasoline engine. Two simulation environments were deployed in this strategy, each with unique advantages. The first was Autonomie, which is a commercial software tool that is well-validated through peer-reviewed studies. This allowed the team to evaluate a wide range of components in a robust simulation framework.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

Hybrid-electric gas turbine generators are considered a promising technology for more efficient and sustainable air transportation. The Ohio State University is leading the NASA University Leadership Initiative (ULI) Electric Propulsion: Challenges and Opportunities, focused on the design and demonstration of advanced components and systems to enable high-efficiency hybrid turboelectric powertrains in regional aircraft to be deployed in 2030. Within this large effort, the team is optimizing the design of the battery energy storage system (ESS) and, concurrently, developing a supervisory energy management strategy for the hybrid system to reduce fuel burn while mitigating the impact on the ESS life. In this paper, an energy-based model was developed to predict the performance of a battery-hybrid turboelectric distributed-propulsion (BHTeDP) regional jet.

Centrifugal compressors for automotive turbochargers must operate over wide speed and flow ranges to provide the required air pressure and mass flow rate to the intake manifold of the internal combustion engines. At a fixed rotational speed, the flow field near the inducer of the impeller becomes increasingly unstable with decreasing flow rate, as the incidence angle grows between the air flow approaching the impeller, relative to the tangent of the main impeller blades at the leading edge. Flow field measurements conducted earlier have revealed that once the incidence angle exceeds a critical value (nearly independent of rotational speed) of approximately 15°, reversed flow near the periphery (blade tips) starts penetrating upstream of the impeller, with a high tangential velocity in the direction of impeller rotation.

The conventional ported shroud recirculation casing treatment elevates narrowband noise at blade pass frequency. A new ported shroud recirculating casing treatment was implemented in Ford’s 3.5L turbo gas engine as Noise Vibration and Harshness (NVH) counter measure to reduce whoosh (broadband flow noise) noise without elevating narrowband noise at blade pass frequency. The new ported shroud design incorporates holes between the main and secondary recirculating passage and a slight cross-sectional area reduction just upstream of the impeller. These design features reduce whoosh noise without elevating the first order and the sixth order tonal noise at blade pass frequency. The new ported shroud design decreases narrowband tonal noise sound pressure level by 3-6 dB in the low to mid flow region compared to the baseline design. Computational Fluid Dynamics (CFD) tools were used to develop this casing treatment design.

As vehicle emissions regulations become increasingly stringent, there is a growing need to accurately model aftertreatment systems to aid in the development of ultra-low NOx vehicles. Common solutions to this problem include the development of complex chemical models or expansive neural networks. This paper aims to present the development process of a simpler Selective Catalytic Reduction (SCR) conversion efficiency Simulink model for the purposes of modeling tail pipe NOx emission levels based on various inputs, temperature shifts and SCR locations, arrangements and/or sizes in the system. The main objective is to utilize this model to predict tail pipe NOx emissions of the EPA Federal Test Procedures for heavy-duty vehicles. The model presented within is focused exclusively on heavy-duty application compression ignition engines and their corresponding aftertreatment setups.

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.

A machine has been developed to measure the complete inertia matrix; mass, center of gravity (CG) location, and all moments and products of inertia. Among other things these quantities are useful in studying engine vibrations, calculation of the torque roll axis, and in the placement of engine mounts. While the machine was developed primarily for engines it can be used for other objects of similar size and weight, and even smaller objects such as tires and wheels/rims. A key feature of the device is that the object, once placed on the test table, is never reoriented during the test cycle. This reduces the testing time to an hour or less, with the setup time being a few minutes to a few hours depending on the complexity of the shape of the object. Other inertia test methods can require up to five reorientations, separate CG measurement, and up to several days for a complete test.

Automobile manufacturers are reducing the weight of their vehicles in order to meet strict fuel economy legislation. To achieve this goal, a combination of different materials such as steel, aluminum and carbon fiber composites are being considered for use in vehicle bodies. The ability to join these different materials is an ongoing challenge and an area of research for automobile manufacturers. Multiridged fasteners are a viable option for this type of multi-material joining. Commercial systems exist and are being used in the industry, however, new ridged nail designs offer the potential for improvement in several areas. The goal of this paper is to prototype and test a safer flat-end fastener whilst not compromising on strength characteristics, to prevent injury to factory workers. The nails were prototyped using existing RIVTAC® nails.

Increasing complexity of automotive systems along with growing safety and performance requirements, is causing development cycle costs to swell. A common solution is to use a Model-Based Design (MBD) approach, particularly using x-In the Loop (xIL) simulation methods for Validation and Verification (V&V). MBD allows efficient workflow from offline control design using high-fidelity models to real time V&V using Hardware-in-the-Loop (HIL) simulations. It is very challenging to reduce the complex non-linear high-fidelity models to real-time capable models for HIL simulation. Current literature does not provide a standard approach for obtaining the HIL-capable reduced model for complex non-linear systems. In this paper we present an approach to perform model reduction in light of HIL-level requirements. The approach is presented using an example of a 10-speed automatic transmission. The system constitutes three subsystems - the hydraulic network, mechanical gearbox, and torque converter.