Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this paper, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this paper, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS

Intelligent Traction Control in Electric Vehicles using a Novel Acoustic Approach for Online Estimation of Road-Tire Friction

Torque control of electric motor via current gives the advantage of simplicity and fast response over the complicated torque control of an internal combustion engine which may depend on several parameters ranging from fuel valve angle to gas pedal position and several delay factors. Although traction control system (TCS) for in-wheel-motor (IWM) configuration electric vehicles (EV) has advantages, the performance of the control system, as in most traction control cases, still depends on (1)accurate estimation of road-tire friction characteristics and (2) measurement of slip ratio requiring expensive sensors for obtaining wheel and chassis velocity. The main contribution of this work is design and integration of an acoustic road-type estimation system (ARTE), which significantly increases the robustness and reduces the cost of TCS in IWM configuration EVs. Unlike complicated and expensive sensor units, the system uses a simple data collection set-up including a low-cost cardioid microphone directed to vicinity of road-tire interface. The acoustic data is then reduced to features such as linear predictive, cepstrum and power spectrum coefficients. For robust estimation, only some of these coefficients are selected based on minimum intra-class variance and maximum inter-class distance criteria to train an artificial neural network (ANN) for classification. The road types can be grouped into: Asphalt, gravel, stone and snow with a correct classification rate of 91% for the test data. The predicted road-type is used to select the correct friction characteristic curve (μ-λ) which helps calculating the appropriate torque command for the particular road-tire condition. The system has been evaluated in extensive simulations and the results show that extreme torque values are supressed stabilising the vehicle for several driving scenarios in a more energy-efficient and robust manner compared to previous systems

A Low-Cost Embedded Data Collection System for Traction Control Systems in Electric Vehicles

The transition from conventional vehicles to electric vehicles (EVs) has increased interest in research in the area of autonomy to prevent traffic accidents. Despite the relevance of the related research to the well-being of the society, commercial vehicles offered by automotive industries often do not provide the openness required for research and realistic experiments. In this paper, we propose the use of a noncommercial electric vehicle, and a novel low-cost embedded (LCE) data collection system for research and education in advanced driver-assistance systems (ADAS). This LCE data system for EV can collect vehicle-dynamics related data and environmental context via a low-cost platform. These inputs are mainly the wheel motor current indicating the torque demand, steering wheel angle, angular wheel velocity, global positioning, 3 axis acceleration, 3 axis rotation and 3 axis magnetics measurements. Using these inputs, we propose the design of a prospective traction control system that would allow for different levels of autonomy. In this work, for traction control of the EV, the maximum transmissible torque estimation method (MTTE) is used. Our experimental results demonstrate a 10% improvement in the maximum slip rate of EV

Validation Of Pre-Adolescent Decision-Making Competence In Turkish Students

The objective of this study was to adapt the Pre-Adolescent Decision-Making Competence Test to Turkish, which was originally developed in English by Weller, Levin, Rose and Bossard (2012) for assessing decision-making competence of children between the ages of 9 and 14. For this purpose; a) the test and instructions were translated into Turkish, b) the Turkish test was administered to a group of 398 students as a pilot, c) retest was administered to a group of 97 students, and finally, d) a group of 382 students was subject to a norm study. The Confirmatory Factor Analysis model created by the data of the pilot administration was well adapted, and one-factor model was verified for construct validity. As the construct of the test was altered, a Confirmatory Factor Analysis was performed on the data obtained from the norm study. A construct similar to that acquired from the data of the first test administration and the results obtained have even relatively better fit indices. Although the reliability values were less than what was expected, Cronbach's Alpha coefficient of internal consistency was similar to the results obtained from the original test.Wo

Validation of Pre-Adolescent Decision-Making Competence in Turkish students

The objective of this study was to adapt the Pre-Adolescent Decision-Making Competence Test to Turkish, which was originally developed in English by Weller, Levin, Rose and Bossard (2012) for assessing decision-making competence of children between the ages of 9 and 14. For this purpose; a) the test and instructions were translated into Turkish, b) the Turkish test was administered to a group of 398 students as a pilot, c) retest was administered to a group of 97 students, and finally, d) a group of 382 students was subject to a norm study. The Confirmatory Factor Analysis model created by the data of the pilot administration was well adapted, and one-factor model was verified for construct validity. As the construct of the test was altered, a Confirmatory Factor Analysis was performed on the data obtained from the norm study. A construct similar to that acquired from the data of the first test administration and the results obtained have even relatively better fit indices. Although the reliability values were less than what was expected, Cronbach's Alpha coefficient of internal consistency was similar to the results obtained from the original test

Soil Pollution in Turkey and Remediation Methods

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