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A Power Split Hybrid Propulsion System for Vehicles with Gearbox

Istituto Motori CNR-Luigi De Simio, Michele Gambino, Sabato Iannaccone
  • Technical Paper
  • 2020-37-0014
To be published on 2020-06-23 by SAE International in United States
New internal combustion engines (ICE) are characterised by increasing maximum efficiency, thanks to the adoption of strategies like Atkinson cycle, downsizing, cylinder deactivation, waste heat recovery and so on. However, the best performance is confined to a limited portion of the engine map. Moreover, electric driving in urban areas is an increasingly pressing request, but battery electric vehicles use cannot be easily widespread due to limited vehicle autonomy and recharging issues. Therefore, in order to reduce ICE vehicle fuel consumption, by decoupling the ICE running from road load, as well as permit energy recovery and electric driving, hybrid propulsion systems are under development. This paper analyses a new patent solution for power split hybrid propulsion system with gearbox. The system comprises an auxiliary power unit, adapted to store and/or release energy, and a planetary gear set which is interposed between the ICE and the gearbox. The system is characterized by a further device coupled with the ICE to modulate the resistance torque, in order to use the auxiliary power unit also for regenerative braking. The…
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Tire NVH Optimization for Future Mobility

CEAT Ltd-Rahul R. Sanghani, Thomas Cherian, Subramaniam Loganathan, Kamal Suhalka, Juban Thomas
  • Technical Paper
  • 2020-01-1520
To be published on 2020-06-03 by SAE International in United States
Vehicle NVH (Noise, Vibration and Harshness) is one of the most critical customer touchpoints which may lead to buying decisions. The importance of Noise inside the cabin is increasing day by day because of the new era of E-mobility and autonomous driving. Noise source could be the engine, powertrain, tyre, suspension components, brake system, etc. depending on driving conditions. Among these, tire noise is being identified as biggest contributor at constant mid-speed driving where engine and powertrain operate at minimum noise and wind noise is also at a moderate level. This driving condition becomes very significant for electric vehicles where engine noise is replaced by motor noise which is a tonal noise at very high frequency. This makes the improvement of tire noise levels quintessential for good cabin acoustic feel. This demands a proactive approach to develop low noise tire platforms for future mobility by leveraging research tools and best practices in the industry. With a greater emphasis on ride and comfort in passenger car vehicles, tyre manufacturers will be challenged to meet stringent harshness…
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Unsettled Topics in Autonomous Vehicle Data Sharing for Verification and Validation Purposes

Caliber Data Labs-Mohsen Khalkhali, Yaser Khalighi
  • Research Report
  • EPR2020007
To be published on 2020-05-15 by SAE International in United States
The race to autonomy has been synonymous with the race to data collection. Autonomous vehicles (AVs) generate terabytes of data per day. Perception engineers use these large datasets to analyze and model the automated driving systems (ADS) that will eventually be put into vehicles that will drive themselves. However, the current industry practices of collecting data by driving on public roads to understand real-world scenarios is not practical and will be unlikely to lead to safe deployment of this technology anytime soon. Estimates show that it could take 400 years for a fleet of 100 AVs to drive enough miles to prove that they are as safe as humans. We, therefore, discuss an unsettled topic of sharing data for verification and validation purposes where – instead of each testing project and organization doing their own tests in isolation and potentially duplicating work – a shared – data culture, business, and technology be developed. This could allow for rapid generation, testing, and sharing of the billions of possible scenarios that are needed to prove practicality and…
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Unsettled Topics in UAV Icing

Norwegian University of Science and Technology-Richard Hann, Tor A. Johansen
  • Research Report
  • EPR2020008
To be published on 2020-04-24 by SAE International in United States
Unmanned aerial vehicles (UAVs) are an emerging technology with a large variety of commercial and military applications. In-flight icing occurs during flight in supercooled clouds or freezing precipitation and is a potential hazard to all aircraft. In-flight icing on UAVs imposes a major limitation on the operational envelope. This report describes the unsettled topics related to UAV icing. First, typical UAV applications and the general hazards of icing are described. Second, an overview of the special technical characteristics of icing on autonomous and unmanned aircraft is given. Third, the operational challenges for flight in icing conditions are discussed. Fourth, technologies for icing protection that mitigate the icing hazard are introduced. Fifth, the tools and methods required to understand UAV icing and to develop aircraft with cold weather capabilities are presented. Finally, an assessment of the current and future regulations regarding icing on UAVs is provided. Icing is a key challenge that the UAV industry needs to address in order to unlock the full potential of this emerging technology. UAVs must be capable of safe and…
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Trajectory Planning and Tracking for Four-Wheel-Steering Autonomous Vehicle with V2V Communication

Jilin University-Fangwu Ma, Yucheng Shen, Jiahong Nie, Xiyu Li, Yu Yang, Jiawei Wang, Guanpu Wu
  • Technical Paper
  • 2020-01-0114
To be published on 2020-04-14 by SAE International in United States
Lane-changing is a typical traffic scene effecting on road traffic with high request for reliability, robustness and driving comfort to improve the road safety and transportation efficiency. The development of connected autonomous vehicles with V2V communication provide more advanced control strategies to research of lane-changing. Meanwhile, four-wheel steering is an effective way to improve flexibility of vehicle. The front and rear wheels rotate in opposite direction to reduce the turning radius to improve the servo agility operation at the low speed while those rotate in same direction to reduce the probability of the slip accident to improve the stability at the high speed. Hence, this paper established Four-Wheel-Steering(4WS) vehicle dynamic model and quasi real lane-changing scenes to analyze the motion constraints of the vehicles. Then, the polynomial function was used for the lane-changing trajectory planning and the extended rectangular vehicle model was established to get vehicle collision avoidance condition. Vehicle comfort requirements and lane-changing efficiency were used as the optimization variables of optimization function and the control of trajectory tracking can be obtained by using…
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Alleviating the Magnetic Effects on Magnetometers using Vehicle Kinematics for Yaw Estimation for Autonomous Ground Vehicles

Michigan Technological University-Ahammad Basha Dudekula, Jeffrey D. Naber
  • Technical Paper
  • 2020-01-1025
To be published on 2020-04-14 by SAE International in United States
Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The present work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. In addition, the error in rate measurements from gyro sensor gets accumulated as the time progress which results in drift in rate measurements and thus affecting the vehicle…
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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 and Odometry data, and object lists from Radar 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 objects-based localization. This approach was successfully tested on FEV’s Smart Vehicle Demonstrator at FEV’s HQ representing 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 or LiDAR alone.
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Energy Efficient Maneuvering of Connected and Automated Vehicles

Southwest Research Institute-Sankar Rengarajan, Scott Hotz, Jayant Sarlashkar, Stanislav Gankov, Piyush Bhagdikar, Michael C. Gross, Charles Hirsch
  • Technical Paper
  • 2020-01-0583
To be published on 2020-04-14 by SAE International in United States
Onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies will allow a vehicle to "know" its future operating conditions with some degree of certainty, greatly narrowing prior information gaps. The increased development of such Connected and Automated Vehicle (CAV) systems, currently used mostly for safety and driver convenience, presents new opportunities to improve the energy efficiency of individual vehicles. The NEXTCAR program is one such initiative by the Advanced Research Projects Agency – Energy (ARPA-E) to developed advanced vehicle dynamics and powertrain control technologies that leverage such connected information streams. Southwest Research Institute (SwRI) in collaboration with Toyota and University of Michigan is currently working on improving energy consumption of a Toyota Prius Prime 2017 by 20%. This paper provides an overview of the various algorithms that have been developed to achieve the energy consumption target. A breakdown of how individual algorithms contribute to the overall target is presented. The team built a specialized test-bed called CAV dynamometer that integrates a traffic simulator and a hub dynamometer for testing the…
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Vehicle Velocity Prediction Using Artificial Neural Network and Effect of Real World Signals on Prediction Window.

Colorado State University-Aaron Rabinowitz, Thomas Bradley
Western Michigan University-Tushar Gaikwad, Farhang Motallebiaraghi, Zachary Asher, Alvis Fong, Rick Meyer
  • Technical Paper
  • 2020-01-0729
To be published on 2020-04-14 by SAE International in United States
Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide the highest velocity prediction fidelity. We developed LSTM deep neural network which uses different groups of datasets collected in Fort Collins. Synchronous data was gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-level transit time and signal phase and timing. Effect of different group of datasets on forward velocity prediction window of 10, 15, 20 and 30 seconds is studied. Developed algorithm is tested…
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In-Situ Measurement of Component Efficiency in Connected and Automated Hybrid-Electric Vehicles

Southwest Research Institute-Peter Lobato, Kyle Jonson, Sankar Rengarajan, Jayant Sarlashkar
  • Technical Paper
  • 2020-01-1284
To be published on 2020-04-14 by SAE International in United States
Connected and automated driving technology is known to improve real-world vehicle efficiency by considering information about the vehicle’s environment such as traffic conditions, traffic lights or road grade. This study shows how the powertrain of a hybrid-electric vehicle realizes those efficiency benefits by developing methods to directly measure transient real-time efficiency and power losses of the vehicle’s powertrain components through chassis-dynamometer testing. This study is a follow-on to SAE Technical Paper 2019-01-0116, Test Methodology to Quantify and Analyze Energy Consumption of Connected and Automated Vehicles, to understand the sources of efficiency gains resulting from connected and automated vehicle driving. A 2017 Toyota Prius Prime was instrumented to collect power measurements throughout its powertrain and driven over a specific driving schedule on a chassis dynamometer. The same driving schedule was then modified to simulate a connected and automated vehicle driving profile, and the sources of vehicle efficiency improvements are analyzed. While conventional powertrain components typically only have two sources and sinks of power, e.g. an input and output shaft, the components of modern hybrid-electric vehicles are…