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Determining the State Of Health [SOH] of Li Ion cell

Sushant Manohar Mutagekar
  • Technical Paper
  • 2019-28-2579
To be published on 2019-11-21 by SAE International in United States
“NuGen Mobility Summit-2019” Paper Title : Determining the State Of Health [SOH] of Li Ion cell Authors: Sushant Mutagekar, Ashok Jhunjhunwala, Prabhjot Kaur Objective Cells age with life. This aging is dependant on various factors like charging/discharging rates, DOD of operation and operating temperature. As the cell ages it undergoes power fade (ability to deliver required power at particular State of Charge [SOC]) and capacity fade (the charge storage capacity of cell). In an Electric Vehicle it is important to know what power shall be demanded from a battery irrespective of what its current SOC is and number of cycles it has undergone. With minimal accuracy and less computational power, it is difficult for a Battery Management System [BMS] to accurately determine SOH; the paper proposes a a precise model that may help. Methodology To understand different cells aging at different conditions, an experiment was setup to simulate various conditions of a cell. • Cycling: Cells of different form factors were cycled continuously at different ambient temperatures, different discharge rates and Depth of discharges. •…
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Design and Performance Criteria Transport Aircraft Portable Megaphones

S-9A Safety Equipment and Survival Systems Committee
  • Aerospace Standard
  • AS4950B
  • Current
Published 2019-08-22 by SAE International in United States
This SAE Aerospace Standard (AS) provides design criteria and performance tests for portable, handheld, battery-powered, electronic megaphones used by aircraft crew members to provide information and guidance in the event of an aircraft emergency or other non-routine situation.
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Developing a Standardized Performance Evaluation of Vehicles with Automated Driving Features

SAE International Journal of Connected and Automated Vehicles

Virginia Polytechnic Institute and State University, USA-Alexis Basantis
Virginia Tech Transportation Institute (VTTI), USA-Zachary Doerzaph, Leslie Harwood, Luke Neurauter
  • Journal Article
  • 12-02-03-0011
Published 2019-08-21 by SAE International in United States
Objectives: The project goal was to create an initial set of standardized tests to explore whether they enable the ongoing evaluation of automated driving features as they evolve over time. These tests focused on situations that were representative of several daily driving scenarios as encountered by lower-level automated features, often called Advanced Driver Assistance Systems (ADAS), while looking forward to higher levels of automation as new systems are deployed. Methods: The research project initially gathered information through a review of existing literature about ADAS and current test procedures. Thereafter, a focus group of industry experts was convened for additional insights and feedback. With this background, the research team developed a series of tests designed to evaluate a variety of automated driving features in currently available implementations and anticipated future variants. Key ADAS available on current production vehicles include adaptive cruise control (ACC), lane keeping assist (LKA), and automatic emergency braking (AEB). Seven of the most automated production vehicles available in 2018 from six manufacturers were subjected to a series of standardized tests that were performed…
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Brake Rating Power Requirements - Truck and Bus

Truck and Bus Brake Systems Committee
  • Ground Vehicle Standard
  • J257_201907
  • Current
Published 2019-07-18 by SAE International in United States

The minimum performance values in this SAE Recommended Practice are applicable to vehicles with brake systems having typical service pressure ranges 0 to 16.6 MPa (0 to 2400 psi) hydraulic or 0 to 945 kPa (0 to 135 psi) air only. SAE J880 not only provides for determining maximum brake rating power capability, but also permits verification of any desired or arbitrary level such as the requirement established herein. The determining criteria for deciding brake rating power capability are:

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Polyamide Type 6-6, Plastic Moldings and Extrusions

AMS P Polymeric Materials Committee
  • Aerospace Material Specification
  • AMS3617F
  • Current
Published 2019-06-13 by SAE International in United States
This specification covers one type of Polyamide Type 6-6 (nylon) thermoplastic resin in the form of moldings and extrusions.
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Radius-of-Load or Boom Angle Indicating Systems

Cranes and Lifting Devices Committee
  • Ground Vehicle Standard
  • J375_201906
  • Current
Published 2019-06-10 by SAE International in United States
This SAE Recommended Practice applies to cranes used in lifting-crane service which are equipped with radius-of-load or boom angle indicating devices.
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Load Indicating Devices in Lifting Crane Service

Cranes and Lifting Devices Committee
  • Ground Vehicle Standard
  • J376_201906
  • Current
Published 2019-06-10 by SAE International in United States
This SAE Recommended Practice applies to cranes equipped with load indicating devices used in lifting crane service.
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Performance Testing and Analysis of Multi-Channel Active Control System for Vehicle Interior Noise Using Adaptive Notch Filter

Tongji University-Lijun Zhang, Xiyu Zhang, Dejian Meng
Published 2019-06-05 by SAE International in United States
It is considered that slow convergence speed and large calculation amount of commonly used adaptive algorithm in the active control system for vehicle interior noise yield noise reduction performance and hardware requirements problems. In this paper, a 4-channel active control of vehicle interior noise based on adaptive notch filter is established, and road test is carried out to test and analyze the performance of the control system. Firstly, the general mathematic model of the multi-channel active control system based on adaptive notch filter is established. The computational complexity of the algorithm is analyzed and compared with that of the FXLMS algorithm. Secondly, a hardware-in-the-loop test bench based on multi-channel adaptive notch filter is set up, to measure the noise reduction performance of ANC system under various working conditions. Finally, the typical test conditions are designed to test the system noise reduction performance, and the road test is carried out and result is analyzed.
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New Performance Metrics for Lidar

Autonomous Vehicle Engineering: May 2019

Indu Vijayan
  • Magazine Article
  • 19AVEP05_07
Published 2019-05-01 by SAE International in United States

Frame-rate measurement is so yesterday. Object-revisit rate and instantaneous resolution are more relevant metrics, and indicative of what a lidar system can and should do, argues a revolutionary in the artificial-perception space.

How do you measure the effectiveness of an intelligent, lidar-based perception system for autonomous vehicles? Conventional evaluation metrics favor frame rate and resolution as the ideal criteria. However, experts at Pleasanton, Calif.-based artificial perception company AEye believe that these criteria are inadequate for measuring the unique capabilities of more advanced lidar systems, nor do they explicitly address real-world problems facing autonomous driving, such as hazard detection and tracking.

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Color and Height Characteristics of Surrogate Grass for the Evaluation of Vehicle Road Departure Mitigation Systems

Indiana University; Purdue University-Qiang Yi, Dan Shen, Jun Lin, Stanley Chien, Lingxi Li, Yaobin Chen
Toyota Motor Corp.-Rini Sherony
Published 2019-04-02 by SAE International in United States
In recent years Road Departure Mitigation Systems (RDMS) is introduced to the market for avoiding roadway departure collisions. To support the performance testing of the RDMS, the most commonly seen road edge, grass, is studied in this paper for the development of standard surrogate grass. This paper proposes a method for defining the resembling grass color and height features due to significant variations of grass appearances in different seasons, temperatures and environments. Randomly selected Google Street View images with grass road edges are gathered and analyzed. Image processing techniques are deployed to obtain the grass color distributions. The height of the grass is determined by referencing the gathered images with measured grass heights. The representative colors and heights of grass are derived as the specifications of surrogate grass for the standard evaluation of RDMS.
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