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Formula SAE Data Acquisition and Detailed Analysis of a Lap

Georgia Southern University-Connor M. Ashford, Aniruddha Mitra
  • Technical Paper
  • 2020-01-0544
To be published on 2020-04-14 by SAE International in United States
Formula SAE (FSAE) is a student design competition organized by SAE International. The competition requires student teams to design and manufacture a formula style race car, to compete against other teams. Testing and validation of the vehicle is an integral part of the design and performance during the competition. Drivers for the collegiate competition are typically at an amateur level. As a result, the human factor plays a significant role in the outcome of dynamic events. In order to reduce uncertainty and improve the general performance, emphasis on driver training is necessary. Instead of overall performance of the driver based on an individual lap, the current research focuses on detailed components of the driver’s actions throughout different sections of the lap. In order to evaluate the performance of each driver in each of these sections, an AiM data acquisition system was mounted on the EMS17R and EMS18R vehicles along with a multitude of sensors, allowing for everything the driver touches to be recorded as well as dynamic forces seen by the vehicles. The data collected…
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Model-Based Calibration of an Automotive Climate Control System

BMW AG-Gerrit Lassahn, Kang Qiu
TU Munich-Patrick Jahn
  • Technical Paper
  • 2020-01-1253
To be published on 2020-04-14 by SAE International in United States
This paper describes a novel approach for modeling an automotive HVAC unit. The model consists of black-box models trained with experimental data from a self-developed measurement setup. It is capable of predicting the temperature and mass flow of the air entering the vehicle cabin at the various air vents. A combination of temperature and velocity sensors is the basis of the measurement setup. A measurement fault analysis is conducted to validate the accuracy of the measurement system. As the data collection is done under fluctuating ambient conditions, a review of the impact of various ambient conditions on the HVAC unit is performed. Correction models that account for the different ambient conditions incorporate these results. Numerous types of black-box models are compared to identify the best-suited type for this approach. Moreover, the accuracy of the model is validated using test drive data. This validation demonstrates the accuracy of the model of 2 K for temperature predictions. Further studies are recommended to quantify the impact of the model inaccuracies on the model-based calibration process.
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Micro-Mobility Vehicle Dynamics and Rider Kinematics during Electric Scooter Riding

Exponent Inc.-Christina MR Garman, Steven G. Como, Ian C. Campbell, Jeffrey Wishart, Kevin O'Brien, Scott McLean
  • Technical Paper
  • 2020-01-0935
To be published on 2020-04-14 by SAE International in United States
Micro-mobility is a fast-growing trend in the transportation industry with stand-up electric scooters (e-scooters) becoming increasingly popular in the United States. To date, there are over 350 ride-share e-scooter programs in the United States. As this popularity increases, so does the need to understand the performance capabilities of these vehicles and the associated operator kinematics. Scooter tip-over stability is characterized by the scooter geometry and controls and is maintained through operator inputs such as body position, interaction with the handlebars, and foot placement. In this study, testing was conducted using operators of varying sizes to document the capabilities and limitations of these e-scooters being introduced into the traffic ecosystem. A test course was designed to simulate an urban environment including sidewalk and on-road sections requiring common maneuvers (e.g., turning, stopping points, etc.) for repeatable, controlled data collection. A commercially available e-scooter was instrumented to measure acceleration and velocity, steering angle, roll angle, and GPS position. Operators ranging from the 15th percentile to the 85th percentile were instrumented with wearable sensors to gain insight into the…
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LiDAR and Camera-Based Convolutional Neural Network Detection for Autonomous Driving

National Research Council Canada-Ismail Hamieh, Ryan Myers, Hisham Nimri, Taufiq Rahman
University of Windsor-Aarron Younan, Brad Sato, Abdul El-Kadri, Selwan Nissan, Kemal Tepe
  • Technical Paper
  • 2020-01-0136
To be published on 2020-04-14 by SAE International in United States
Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines. Performance of the system is scrutinized with regards to hardware capability, software reliability, and real-time performance. The final product is a mobile-platform capable of identifying pedestrians, cars, trucks and cyclists.
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In-Vehicle Diagnostic System for Prognostics and OTA Updates of Automated / Autonomous Vehicles

Softing Automotive Electronics GmbH-Peter Subke, Muzafar Moshref, Julian Erber
  • Technical Paper
  • 2020-01-1373
To be published on 2020-04-14 by SAE International in United States
The increasing complexity of microcontroller-based automotive E/E systems that control road-vehicles and non-road mobile machinery comes with increased self-diagnosis functions and diagnosability via external test equipment (diagnostic tester).Technicians in the development, production and service depend on diagnostic test equipment that is connected to the E/E system and performs diagnostic communication. Examples of use cases of diagnostic communication include but are not limited to condition monitoring, data acquisition, (guided) fault finding and flash programming.More and more functions of a modern vehicle are realized by software (firmware). Powerful multicore servers replace the numerous control units and many control unit functions can be performed directly by smart sensors and actuators.New E/E system architectures come with increased self-diagnostic capabilities. They automatically perform tests, log diagnostic data and push such data for prognostics purposes and condition (health) monitoring to the cloud. They also support over-the-air firmware updates (FOTA).This paper describes the components of an E/E system that is equipped with an in-vehicle diagnostic tester. The tester consists of standardized components, including MVCI-Server (ISO 22900), ODX (ISO 22901), OTX (ISO 13209)…
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Quantifying Engine Braking for Various Common Street Motorcycles

Collision and Injury Dynamics, Inc.-Henricus Jansen, Beau LeBlanc, Christopher Wilhelm, Tyler Shaw, Alvin Lowi
  • Technical Paper
  • 2020-01-0880
To be published on 2020-04-14 by SAE International in United States
Motorcycle engine braking was measured in each forward gear for a cross-section of typical street motorcycles. Using GPS data acquisition and video, deceleration relative to speed was examined. Motorcycle characteristics included various engine displacements and types of motorcycle. The data acquired will give more insight into the extent which engine braking is a factor for deceleration, a topic which has not been addressed in a peer-reviewed journal article to this date.
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Driving Safety Performance Assessment Metrics for ADS-Equipped Vehicles

Exponent Inc., Arizona State University-Jeffrey Wishart, Steven Como
Intel-Maria Elli, Jack Weast
  • Technical Paper
  • 2020-01-1206
To be published on 2020-04-14 by SAE International in United States
The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed.A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations. These metrics, which include metrics from on- and off-board data sources, allow the driving safety performance of an AV to be measured in a variety of situations, including crashes, potential conflicts, and near misses. These measurements enable the evaluation of temporal flows and the quantification of key aspects of driving safety performance. The identification and exploration of metrics focusing explicitly on AVs…
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Robust xEV Battery State-of-Charge Estimator Design Using a Feedforward Deep Neural Network

FCA US LLC-Pawel Malysz, Oliver Gross
McMaster Automotive Res. Centre-Carlos Vidal, Phillip Kollmeyer, Mina Naguib
  • Technical Paper
  • 2020-01-1181
To be published on 2020-04-14 by SAE International in United States
Battery state-of-charge (SOC) is critical information for the vehicle energy management system and must be accurately estimated to ensure reliable and affordable electrified vehicles (xEV). However, due to the nonlinear temperature, health, and SOC dependent behaviour of Li-ion batteries, SOC estimation is still a significant automotive engineering challenge. Traditional approaches to this problem, such as electrochemical models, usually require precise parameters and knowledge from the battery composition as well as its physical response. In contrast, neural networks are a data-driven approach that requires minimal knowledge of the battery or its nonlinear behaviour. The objective of this work is to present the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The method includes a description of data acquisition, data preparation, development of an FNN, FNN tuning, and robust validation of the FNN to sensor noise. To develop a robust estimator, the FNN was exposed, during training, to datasets with errors intentionally added to the data, e.g. adding cell voltage variation of ±4mV, cell current variation of ±110mA, and temperature variation…
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Analysis of Personal Routing Preference from Probe Data in Cloud

Toyota Motor Corporation-Xin Jin, Taiki Nakamura
ZENRIN DataCom Co., Ltd.-Toshinori Takayama, Ai Yashiro
  • Technical Paper
  • 2020-01-0740
To be published on 2020-04-14 by SAE International in United States
Routing quality always dominates the top 20% of in vehicle- navigation customer complaints. In vehicle navigation routing engines do not customize results based on customer behavior. For example, some users prefer the quickest route while some prefer direct routes. This is because in vehicle navigation systems are traditionally embedded systems. Toyota announced that new model vehicles in JP, CN, US will be connected with routing function switching from the embedded device to the cloud in which there are plenty of probe data uploaded from the vehicles. Probe data makes it possible to analyze user preferences and customize routing profile for users. This paper describes a method to analyze the user preferences from the probe data uploaded to the cloud. The method includes data collection, the analysis model of route scoring and user profiling.Furthermore, the evaluation of the model will be introduced at the end of the paper. The analysis not only focuses on the routes chosen by the user but also compares with the ones not chosen for the same ODs using multivariate analysis on…
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Sensorless Individual Cell Temperature Measurement by Means of Impedance Spectroscopy Using Standard Battery Management Systems of Electric Vehicles

Ruhr-University Bochum-Peter Haussmann, Joachim Melbert
  • Technical Paper
  • 2020-01-0863
To be published on 2020-04-14 by SAE International in United States
Lithium ion technology is state of the art for actual hybrid and electrical vehicles. It is well known that lithium ion performance and safety characteristics strongly depend on temperature. Thus, reliable temperature measurement and control concepts for lithium ion cells are mandatory for applications in electrical cars. Temperature sensors for all individual cells increase the battery complexity and cost of a battery management system. Normally, temperature is measured on module level in current battery packs, without observation of the individual cell temperature. Sensorless cell impedance-based temperature measurement concepts have been published and are validated in laboratory studies. Dedicated test equipment is usually applied, which is not useful for automotive series application. This work describes a practical approach to enable impedance-based sensorless internal temperature measurement for all individual cells using state-of-the art battery management system components. Excitation is generated by DC to DC converters of a standard commercial active balancing systems. For data acquisition, also an established commercial battery monitoring circuit unit is used. To overcome bandwidth limitations, a sub-sampling scheme is presented, which allows to…