scholarly journals Validation and Refinement of a Steering Friction Increase Detection Algorithm Using Test Drive Data

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Arash Mohtat ◽  
Graeme Garner ◽  
Wen-Chiao Lin ◽  
Naser Mehrabi
Keyword(s):  
State Parameter ◽  
Detection Algorithm ◽  
Active Safety ◽  
Direct Estimate ◽  
Test Drive ◽  
Conventional Vehicle ◽  
Power Steering ◽  
Safety Features ◽  
Noise Factors ◽  
Fault Diagnosis And Prognosis

The Electric Power Steering (EPS) System provides steering assist in conventional vehicle driving and is the main actuator for vehicle lateral control in active safety features. While the driver can sometimes compensate for reduced or loss of steering assist caused by EPS mechanical or electrical degradations, it may become very difficult to steer for larger vehicles. Furthermore, active safety functions cannot control the vehicle effectively for lateral motions without a healthy EPS system. Hence, comprehensive EPS system fault monitoring is essential for the next generation of vehicles. Previous works have utilized computer simulation and hardware-in-the-loop experiments to develop fault diagnosis and prognosis algorithms for electrical and mechanical failures in EPS systems. Using test drive data collected, this paper validates and refines a previously developed algorithm designed for detecting increases in EPS system internal mechanical friction. The data include 215 minutes of natural driving with different speeds and steering maneuvers. Noise factors such as tire type and level of friction introduced are also considered. The previous algorithm is refined to enhance performance addressing issues of time delays and parameter uncertainty specific to the previous model-based algorithm. Specifically, a Kalman filter-based joint state-parameter estimator that uses a simplified vehicle dynamic model is developed to provide a direct estimate of steering friction increase. Data collected from test drives indicate that the refined algorithm can robustly indicate a friction increase before an average human driver notices a difference in steering feel.

scholarly journals Electric Power Steering Power Circuit Health Assessment and Mitigation Strategy

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wen-Chiao Lin ◽  
Graeme Garner ◽  
Yat-Chung Tang ◽  
Arash Mohtat
Keyword(s):  
Electric Power ◽  
Supervisory Control ◽  
Steering System ◽  
Efficient Design ◽  
Electric Power Steering ◽  
Test Drive ◽  
Power Circuit ◽  
Driving Experience ◽  
Power Steering ◽  
Circuit Resistance

With recent developments of energy efficient design and control for electric motors, electrical subsystems and components have become integral parts of main actuators in vehicle systems (e.g., steering and propulsion systems). To ensure proper vehicle operations, it is important to make sure that electrical power is properly transmitted through the power circuit from vehicle power source to the electric motor. However, degradation in the power circuit health, which often manifests itself as increased resistance, may affect power transmission and degrade the system performance. For example, in Electric Power Steering (EPS) systems, if the EPS power circuit resistance is increased and the EPS is drawing power to assist the driver, voltage at the EPS module will drop significantly, causing the EPS to reset and, consequently, Loss of Assist (LOA) incidents. Due to compliance in the steering system and suspension design, drivers often feel that the steering system is fighting back when an LOA incident occurs. While previous work has partially addressed this issue by developing algorithms that estimate resistance increase in EPS power circuits, this paper further validates and refines the algorithms for vehicle on-board and off-board implementations using test drive data collected. Since on-board and off-board implementations impose different limits on signal sampling rates, a total of 250 and 465 minutes of data are respectively collected with various vehicle speeds and steering maneuvers. Moreover, a supervisory control solution, referred to as EPS Anti-Loss-of-Assist (ALOA), is proposed that gradually and proactively reduces EPS torque assist as resistance in the EPS power circuit increases so that the EPS voltage is kept above a resetting threshold. Stationary steering tests of the proposed solution as well as demonstrations on parking lot maneuvers at General Motors Milford Proving Grounds are conducted. The stationary steering tests and demonstrations show that, with the proposed supervisory control, negative effects of increased EPS power circuit resistance can be mitigated without noticeable changes in normal driving experience.

scholarly journals On board Driver Monitoring System with Safety Enhanced Brake System

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1844-1849
Keyword(s):  
Research Work ◽  
Health Condition ◽  
Steering Wheel ◽  
Active Safety ◽  
Road Accidents ◽  
Emergency Situations ◽  
Compression Stroke ◽  
Safety Features ◽  
Safety Systems ◽  
Additional Safety

Recent automobile vehicles require additional safety features to enhance the active safety. Due to lack of safety systems in vehicles road accidents are on the rise. The major cause of collision far 80% of accidents is drivers fault as cited by the ministry of road accidents of India. The current research work is carried out to analyze the fault of the driver and to measures the health condition of the driver by placing throb sensor and temperature sensor in steering wheel so as to slow down the vehicle by using Jake brake during abnormal health issue. The proposed systems were analyzed for different category of the condition of driver to improve the safety system technology. When triggered the exhaust valve is opened after the compression stroke enable to escape of compressed air from the cylinders to slowdown the vehicle which prevents the accidents in emergency situations.

scholarly journals Evaluation of an Active Safety Light using Virtual Test Drive within Vehicle in the Loop

2010 ◽  
Author(s):  
Yvonne Laschinsky ◽  
Kilian von Neumann-Cosel ◽  
Mark Gonter ◽  
Christian Wegwerth ◽  
Rolf Dubitzky ◽  
...  
Keyword(s):  
Active Safety ◽  
Test Drive ◽  
Virtual Test

Optical roadsign recognition to improve active safety features

1993 ◽  
Author(s):  
Laurent Guibert ◽  
Gilles Keryer ◽  
Mondher Attia
Keyword(s):  
Active Safety ◽  
Safety Features

Powersplit Hybrid Electric Vehicle Control With Data Dependent Systems Forecasting

2003 ◽  
Author(s):  
Richard T. Nesbitt ◽  
Sudhakar M. Pandit ◽  
Christian M. Muehlfeld
Keyword(s):  
Hybrid Electric Vehicle ◽  
Power Flow ◽  
Current Data ◽  
General Motors ◽  
Time Step ◽  
Test Vehicle ◽  
Conventional Vehicle ◽  
Power Steering ◽  
Drive Motor ◽  
Sample Time

The focus of this paper is on the implementation of Data Dependent Systems (DDS) forecasting in to the control algorithm of the 2001 Michigan Tech Future Truck. The 2001 MTU Future Truck is a 2000 model year Chevrolet Suburban and utilizes a powersplit transmission, which is similar to the Toyota Prius, for its hybrid conversion. The main source of propulsion comes from a General Motors, all aluminum block, 3.5L V-6. In the Future Truck, the accessory current is not directly measured, so it must be calculated from the measured motor current, generator current and battery current. Accessory current is defined as the current used by all of the high voltage components such as the power steering and AC compressor, except the primary drive motor. In order for the vehicle to be charge sustaining, the generator must produce the same amount of power consumed by the accessories and the drive motor. This calculation will only indicate what the accessory load was at the previous sample time and not what the accessory load will be at the current sample time. When it comes to control of the vehicle, this creates a lag, and the controls will undershoot or overshoot the desired accessory current, which creates inefficiencies due to excessive power flow into and out of the battery pack. In order to better understand the accessory load, Data Dependent Systems (DDS) modeling was done on accessory current data collected from the test vehicle, and an AR(26) model was concluded to be adequate, based on the residual sum of squares (RSS) and unified auto-correlations. The DDS modeling of the accessory current also led to the forecasting of the accessory loads. This helps keep battery use to a minimum by allowing the generator to create the correct amount of power, at that time step, to operate the accessories. Accessory draw from the batteries and generator overshoot going into the batteries is minimized and therefore the overall efficiency of the vehicle goes up. The vehicle was tested on a 50-mile circuit including city and highway driving and elevation changes. The results from the test vehicle showed a power savings of 892 kJ/hour which improved the fuel economy by 3 mpg over stock. The charge sustainability of the vehicle was also achieved which means the range of the vehicle is only limited by the fuel mileage, similar to a conventional vehicle.

Lane Detection Algorithm at Night Based-on Distribution Feature of Boundary Dots for Vehicle Active Safety

2012 ◽  
Vol 11 (5) ◽  
pp. 642-646 ◽  
Author(s):  
Ronghui Zhang ◽  
Haiwei Wang ◽  
Xi Zhou ◽  
Lei Wang ◽  
Tonghai Jiang
Keyword(s):  
Detection Algorithm ◽  
Lane Detection ◽  
Active Safety ◽  
Vehicle Active Safety

scholarly journals Extraordinary Passive Safety in Cars Using a Sensor Network Model

Ingeniería ◽  
2022 ◽  
Vol 26 (3) ◽  
pp. 479-492
Author(s):  
José Sergio Ruiz Castilla ◽  
Farid García Lamont
Keyword(s):  
Sensor Network ◽  
Automobile Industry ◽  
Research Work ◽  
Passive Safety ◽  
Active Safety ◽  
Extreme Temperatures ◽  
Seat Belts ◽  
Fire Smoke ◽  
Safety Features ◽  
Future Work

Context:  The automobile industry has included active and passive safety. Active safety incorporates elements to avoid crashes and collisions. Some elements are ABS brakes and stabilization bars, among others. On the other hand, passive safety avoids or minimizes damage to the occupants in the event of an accident. Some passive safety features include seat belts and front and curtain airbags for the driver and other occupants. Method: In this research work, we propose a new category called Extraordinary Passive Safety (XPS). A model of a sensor network was designed to inspect the conditions inside the car to detect fire, smoke, gases, and extreme temperatures. The sensors send data to a device (DXPS) capable of receiving and storing the data. Results: Each sensor collects data and sends it to the DXPS every period. The sensor sends 0s while there is no risk, and 1s when it detects a risk. When the DXPS receives a 1, the pattern is evaluated, and the risk is identified. Since there are several sensors, the reading pattern is a set of 0s (000000). When a pattern with one or more 1s (000100, 010101) is received, the DXPS can send an alert or activate a device. Conclusions: The proposed solution could save the lives of children left in the car or people trapped when the car catches fire. As future work, it is intended to define the devices to avoid or minimize damage to the occupants such as oxygen supply, gas extraction, regulating the temperature, among others.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Sign in / Sign up

Export Citation Format

Share Document