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Autonomous Vehicle Multi-Sensors Localization in Unstructured Environment

FEV North America Inc.-Qusay Alrousan, Hamzeh Alzu'bi, Andrew Pfeil, Tom Tasky
  • Technical Paper
  • 2020-01-1029
To be published on 2020-04-14 by SAE International in United States
Autonomous driving in unstructured environments is a significant challenge due to the inconsistency of important information for localization such as lane markings. To reduce the uncertainty of vehicle localization in such environments, sensor fusion of LiDAR, Radar, Camera, GPS/IMU, and Odometry sensors is utilized. This paper discusses a hybrid localization technique developed using: LiDAR-based Simultaneous Localization and Mapping (SLAM), GPS/IMU, Odometry data, and object lists from Radar, LiDAR, and Camera sensors. An Extended Kalman Filter (EKF) is utilized to fuse data from all sensors in two phases. In the preliminary stage, the SLAM-based vehicle coordinates are fused with the GPS-based positioning. The output of this stage is then fused with the object-based localization. This approach was successfully tested on FEV’s Smart Vehicle Demonstrator at FEV’s HQ. It represented a complicated test environment with dynamic and static objects. The test results show that multi-sensor fusion improves the vehicle’s localization compared to GPS/IMU or LiDAR alone.
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Investigation of Diesel/Natural Gas RCCI Combustion Using Multiple Reaction Mechanisms at Various Engine Operating Conditions

FEV North America Inc.-Mufaddel Dahodwala, Satyum Joshi, Erik Koehler, Michael Franke, Dean Tomazic
Michigan Technological University-Jeffrey Naber
  • Technical Paper
  • 2020-01-0801
To be published on 2020-04-14 by SAE International in United States
Past experimental studies conducted by the current authors on a 13 liter 16.7:1 compression ratio heavy-duty diesel engine have shown that diesel /natural gas Reactivity Controlled Compression Ignition (RCCI) combustion targeting low NOx emissions becomes progressively difficult to control as the engine load is increased due to difficulty in controlling reactivity levels at higher loads. For the current study, CFD investigations were conducted using the SAGE combustion solver in Converge with the application of Rahimi mechanism. Studies were conducted at a load of 5 bar BMEP to validate the simulation results against RCCI test data. In the low load study, it was found that the Rahimi mechanism was not able to predict the RCCI combustion behavior for diesel injection timings advanced beyond 30bTDC. This behavior was found at multiple engine speed and load points. To resolve this, multiple reaction mechanisms were evaluated and a new reaction mechanism that combines the GRI Mech 3.0 mechanism with the Chalmers mechanism was proposed. This mechanism was found to accurately predict the ignition delay and combustion behavior with early…
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Fuel Properties and their Impact on Stochastic Pre-Ignition Occurrence and Mega Knock Severity in Turbocharged Direct Injection Engines

FEV North America Inc.-Patrick Haenel, Dean Tomazic, Henning Kleeberg, Joseph Ciaravino
  • Technical Paper
  • 2020-01-0614
To be published on 2020-04-14 by SAE International in United States
Stochastic Pre-Ignition (SPI) or Low Speed Pre-Ignition (LSPI) is an abnormal combustion event that can occur during the operation of modern, highly boosted direct-injection gasoline engines. This abnormal combustion event is characterized by an undesired and early start of combustion that is not initiated by the spark plug. Early SPI events can subsequently lead to violent auto-ignitions that are referred to as Mega- or Super-Knock in literature and have the potential to severely damage engines in the field. Numerous studies to analyze impact factors on SPI occurrence and severity have been conducted in recent years. While initial studies have focused strongly on engine oil formulation, calibration and engine design and their respective impact on SPI initiation, the impact of physical and chemical properties of the fuel have also become of interest in recent years. There is still significant uncertainty about the best way to characterize a fuels impact on SPI occurrence and severity though. We therefore performed an experimental study that attempts to link fuel characteristics to SPI event occurrence as well as assesses their…
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Trade-Off Analysis and Systematic Optimization of a Heavy-Duty Diesel Hybrid Powertrain

FEV North America Inc.-Satyum Joshi, Mufaddel Dahodwala, Erik W. Koehler, Michael Franke, Dean Tomazic
Michigan Technological University-Jeffrey Naber
  • Technical Paper
  • 2020-01-0847
To be published on 2020-04-14 by SAE International in United States
While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain. However, certain heavy-duty applications, such as Class 6-7 urban vocational trucks, can benefit from hybridization due to their transient operating profiles and relatively lower vehicle weight. While many studies have quantified the fuel consumption benefits of hybridization in this segment, very few studies have outlined the arduous process of selection and…
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LiDAR-Based Urban Autonomous Platooning Simulation

FEV North America Inc.-Hamzeh Alzu'bi, Tom Tasky
  • Technical Paper
  • 2020-01-0717
To be published on 2020-04-14 by SAE International in United States
The technological advancements of Advanced Driver Assistance Systems (ADAS) sensors enable the ability to; achieve autonomous vehicle platooning, increase the capacity of road lanes, and reduce traffic. This article focuses on developing urban autonomous platooning using LiDAR and GPS/IMU sensors in a simulation environment. Gazebo simulation is utilized to simulate the sensors, vehicles, and testing environment. Two vehicles are used in this study; a Lead vehicle that follows a preplanned trajectory, while the remaining vehicle (Follower) uses the LiDAR object detection and tracking information to mimic the Lead vehicle. The LiDAR object detection is handled in multiple stages: point cloud frame transformation, filtering and down-sampling, ground segmentation, and clustering. The tracking algorithm uses the clustering information to provide position and velocity of the Lead vehicle which allows for vehicle platooning. This paper covers the LiDAR object detection and tracking algorithms as well as the autonomous platooning control algorithms which were tested in a simulation environment. Test results illustrate that the Follower vehicle was able to attain the autonomous platooning based on the LiDAR data.
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LiDAR-Based Predictive Cruise Control

FEV North America Inc.-Hamzeh Alzu'bi, Anthony T. Jarbo, Qusay Alrousan, Tom Tasky
  • Technical Paper
  • 2020-01-0080
To be published on 2020-04-14 by SAE International in United States
Advanced Driver Assistance Systems (ADAS) enable safer driving by relying on the inputs from various sensors including Radar, Camera, and LiDAR. One of the newly emerging ADAS features is Predictive Cruise Control (PCC). PCC aims to optimize the vehicle’s speed profile and fuel efficiency. This paper presents a novel approach of using the point cloud of a LiDAR sensor to develop a PCC feature. The raw point cloud is utilized to detect objects in the surrounding environment of the vehicle, calculate grade of the road, and plan the route in drivable areas. This information is critical for the PCC to define the optimal speed profile of the vehicle while following the planned path. This paper also discusses the developed algorithms of the LiDAR data processing and PCC controller. These algorithms were tested on FEV’s Smart Vehicle Demonstrator platform. Test results show that the proposed PCC was implemented successfully, allowing the vehicle to adapt its speed based on the processed data of the LiDAR sensor.
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Efficiency Evaluation of Lower Viscosity ATF in a Planetary Automatic Transmission for Improved Fuel Economy

FEV North America Inc.-Thomas D'Anna, Jason E. Murtagh, Thomas Wellmann
FCA US LLC-Haiying Tang
Published 2019-04-02 by SAE International in United States
With continued industry focus on reducing parasitic transmission and driveline losses, detailed studies are required to quantify potential enablers to improve vehicle fuel economy. Investigations were undertaken to understand the influence of lower viscosity Automatic Transmission Fluids (ATF) on transmission efficiency as compared with conventional fluids. The objectives of this study were to quantify the losses of lower viscosity ATF as compared with conventional ATF, and to understand the influence of ATF properties including viscosities, base oil types, and additive packages on fuel efficiency.The transmission efficiency investigations were conducted on a test bench following a vehicle-based break-in of the transmission using a prescribed drive cycle on a chassis dynamometer. At low temperature, the lower viscosity ATF showed a clear advantage over the conventional ATF in both spin loss and loaded efficiency evaluations. At high temperature, mixed results were obtained; it appeared the chemistry of ATF influenced the results.Overall, using the low viscosity fluid tends to improve loss behavior, but the benefits can be offset if the transmission hardware employed is not specifically designed for low…
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Autonomous Driving Development Rapid Prototyping Using ROS and Simulink

FEV North America Inc.-Hamzeh Alzu'bi, Sarika Nagaraj, Qusay Alrousan, Alanna Quail
Published 2019-04-02 by SAE International in United States
Recent years have witnessed increasing interest in Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) development, motivating the growth of new sensor technologies and control platforms. However, to keep pace with this acceleration and to evaluate system performance, a cost and time effective software development and testing framework is required. This paper presents an overview utilizing Robotic Operating System (ROS) middleware and MATLAB/Simulink® Robotics System Toolbox to achieve these goals. As an example of employing this framework for autonomous development and testing, this article utilizes the FEV Smart Vehicle Demonstrator. The demonstrator is a reconfigurable and modular platform highlighting the power and flexibility of using ROS and MATLAB/Simulink® for AD rapid prototyping. High-level autonomous path following and braking are presented as two case studies. Test results demonstrate the portability, maintainability, and reliability of the presented system.
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Analysis of the Impact of Production Lubricant Composition and Fuel Dilution on Stochastic Pre-Ignition in Turbocharged, Direct-Injection Gasoline Engines

FEV North America Inc.-Patrick Haenel, Rob de Bruijn, Dean Tomazic, Henning Kleeberg
Published 2019-04-02 by SAE International in United States
The occurrence of abnormal combustion events leading to high peak pressures and severe knock can be considered to be one of the main challenges for modern turbocharged, direct-injected gasoline engines. These abnormal combustion events have been referred to as Stochastic Pre-Ignition (SPI) or Low-Speed Pre-Ignition (LSPI). The events are characterized by an undesired, early start of combustion of the cylinder charge which occurs before or in parallel to the intended flame kernel development from the spark plug. Early SPI events can subsequently lead to violent auto-ignitions that are often referred to as Mega- or Super-Knock. These heavy knock events lead to strong pressure oscillations which can destroy production engines within a few occurrences. SPI occurs mainly at low engine speed and high engine load, thus limiting the engine operating area that is in particular important to achieve good drivability in downsized engines. Recent experimental SPI studies have linked this phenomenon strongly to engine oils.While numerous studies have been published using target blended oils, the presented study focuses on the impact of lubricants with production level…
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Reduction of Parasitic Losses in Front-End Accessory Drive Systems: Part 2

SAE International Journal of Engines

FEV North America Inc.-Marek Tatur, Dean Tomazic, Kiran Govindswamy
FEV Group GmbH-Stefan Trampert
  • Journal Article
  • 2018-01-0326
Published 2018-04-03 by SAE International in United States
Demanding CO2 and fuel economy regulations are continuing to pressure the automotive industry into considering innovative powertrain and vehicle-level solutions. Powertrain engineers continue to minimize engine internal friction and transmission parasitic losses with the aim of reducing overall vehicle fuel consumption.In Part 1 of the study (2017-01-0893) described aspects of the test stand design that provides flexibility for adaptation to various test scenarios. The results from measurements for a number of front-end accessory drive (FEAD) components were shown in the context of scatterbands derived from multiple component tests. Key results from direct drive and belt-driven component tests were compared to illustrate the influence of the belt layout on mechanical efficiency of the FEAD system.The second part of the series will focus exclusively on the operation of the alternator. Two main elements of the study are discussed. The first part explores tests performed to evaluate the main design aspects of the component. Different belt designs, routing, and tension levels were tested and compared. A resulting matrix allows to determine an optimized belt design and layout for…
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