Browse Topic: Traction control
Inclement weather can have a significant impact on surface transportation systems. It can result in hazardous conditions for travelers due to poor visibility, or wet or icy roadways. Weather applications have the potential to provide additional data to surface transportation infrastructure owners and operators, allowing them to better assess the impacts of the weather environment on or around the roadway and to better manage the surface transportation system. Such weather applications can: Collect road weather data from connected vehicles and mobile devices, increasing the number of data sources available. Provide road weather related traveler information to travelers via connected vehicles and devices, such as when and where a hazardous condition exists. Provide the ability to manage road weather response on specific roadways. This SAE Standard specifies interface requirements between vehicles and infrastructure for weather applications, including detailed systems engineering documentation (needs and requirements mapped to appropriate message exchanges). The purpose of this SAE Standard is to enable interoperability supporting these weather applications over a communications technology agnostic interface.
The current simulation models of EV and ICE Vehicles are well known in industry for their use in estimating the fuel economy or Range benefits because of controller calibrations and component sizing. However, there is a gap in understanding the behavior of accessories such as HVAC, power steering and other such auxiliary loads and the energy losses associated with them. Impact of thermal behavior of electronics on vehicle range also needs to be studied in detail. These kinds of studies help OEM and tier 1 manufactures in improving their design concepts significantly with minimum cost and development time. Hence, the focus of this study is on building simulation models of thermal, electrical, traction and control circuits of a typical electric vehicle. These models are then integrated, and analysis is performed to understand vehicle system level performance metrics. Individual models have been built for HVAC and thermal circuit of on EV in AMESim, HV and LV electrical power distribution in Simulink and for vehicle powertrain using powertrainblockset in Simulink. The aim of this paper is to demonstrate the importance of simulation models that capture both traction, accessories and energy consumption split between them. Different challenges in building, integrating and cosimulation of models, impact of model fidelities on runtimes and accuracy of results have been discussed. Modelling aspects related to HVAC, cooling and heating loops of electronics devices, battery and traction control, are also included. Finally, the results over a typical drive cycle are presented.
ABSTRACT In this paper, a conceptually new research direction of the tire slippage analysis is provided as a new technological paradigm for agile tire slippage control. Specifically, the friction coefficient-slippage dynamics is analyzed and its characteristic parameters are introduced. Next, the nonlinear relation between the wheel torque and the tire instantaneous rolling radius incorporating the longitudinal elasticity factor is analyzed. The relation is shown to be related to the tire slippage. Further, its importance is clarified by deriving its dynamics and specifically, the instruction is given how it can be utilized to control slippage. Finally, the indices are introduced to assess the mobility and agility of the wheel in order to achieve optimal response to severe terrain conditions. The indices comprise of the introduced friction coefficient-slippage characteristic parameters. Citation: M. Ghasemi, V. Vantsevich, D. Gorsich, J. Goryca, A. Singh, L. Moradi, “Physics Based Single-Wheel Module Slippage Assessment for Autonomous Control Design”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 10-12, 2021.
As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control. Torque-based control is critical in modern cars and trucks for traction control, stability control, advanced driver assistance systems, and autonomous vehicle systems. Closed loop torque-based engine control systems require feedback signal(s); indicated mean effective pressure (IMEP) is a useful signal but is costly to measure directly with in-cylinder pressure sensors. Previous work has been done in torque and IMEP estimation using crankshaft acceleration and ion sensors, but these systems lack accuracy in some operating ranges and the ability to estimate cycle-cycle variation. In this study, we show that a data driven system to estimate IMEP using frequency content of crank acceleration, exhaust pressure, and ion current with optimized data windowing can effectively estimate individual cylinder cycle-cycle variation in IMEP over some engine operating regions. Fourier Transforms are used to extract features from the angle domain sensors that are useful for IMEP estimation. A neural network is used to estimate IMEP from those features. Pattern search and grid search algorithms are used to optimize feature extraction, network structure, and network training hyper-parameters with dual objectives of minimizing error and network complexity. These derivative free optimization techniques drove the IMEP estimation error down to 16 kPa over a transient drive cycle (using production possible sensors) and allow it to estimate cycle-cycle variation in some conditions.
A TCS strategy of electric vehicle with 4 in-wheel motors is proposed in this paper. The control method consists of three parts: target slip rate calculation, target torque calculation and coordination control. By using Lyapunov stability analysis algorithm, the target slip rate boundary which makes the system stable is obtained. The target torque of each wheel is calculated by PI controller. According to the engineering experience, the TCS coordinated control strategy under split friction coefficient (split-μ) road, and friction coefficient jump(μ jump) road is proposed. The test results show that this strategy can improve the acceleration comfort and yaw stability of vehicles on uniform low friction coefficient (low μ) , split-μ and μ jump road.
A Direct Yaw-Moment Control (DYC) logic for a rear-wheel-drive electric-powered vehicle is proposed. The vehicle is a Formula SAE (FSAE) type race car, with two electric motors powering each rear wheel. Vehicle baseline balance is neutral at low speeds, for increased maneuverability, and increases understeering at high speeds (due to the aerodynamic configuration) for stability. A controller that can deal with these yaw response variations, modelling uncertainties, and vehicle nonlinear behavior at limit handling is proposed. A two-level control strategy is considered. For the upper level, yaw rate and sideslip angle are considered as feedback control variables and a cubic-error Proportional Derivative (PD) controller is proposed for the feedback control. For the lower level, a traction control algorithm is used, together with the yaw moment requirement, for torque allocation. Performance of the controller was evaluated using the Sine with Dwell maneuver and also a lap time simulation around a racetrack. A physically existing go-kart track is modelled for this purpose. Track and vehicle models are built using IPG CarMaker, and a control algorithm is implemented in MATLAB Simulink. Simulations are performed using IPG Racing Driver, varying the learning rate toward aggressive driving and increasing the combined acceleration target, to achieve the best lap times. Simulations results demonstrate the proposed DYC logic using the PD cubic controller substantially improves the simulated vehicle stability on the Sine with Dwell test and around the racetrack. Furthermore, the implementation of the controllers enables a gain of approximately 2 s over a 37 s lap time on the racetrack and allows a more aggressive driving style. As simulations are performed using a driver model, this gain in stability and speed might apply to either a human-operated or autonomous race car. Moreover, the controller could be used in a passenger vehicle, enhancing its safety and maneuverability.
This standard specifies the system requirements for an on-board vehicle-to-vehicle (V2V) safety communications system for light vehicles1, including standards profiles, functional requirements, and performance requirements. The system is capable of transmitting and receiving the SAE J2735-defined basic safety message (BSM) [1] over a dedicated short range communications (DSRC) wireless communications link as defined in the Institute of Electrical and Electronics Engineers (IEEE) 1609 suite and IEEE 802.11 standards [2] to [6].
Wheel slip control is crucial to active safety control systems such as Traction Control System (TCS) and Anti-lock Braking System (ABS) that ensure vehicle safety by maintaining the wheel slip in a stable region. For this reason, a wide variety of control methods has been implemented by both researchers and in the industry. Moreover, the use of new electro-hydraulic or electro-mechanical brakes, and in-wheel electric motors allow for a more precise wheel slip control, which should further improve the vehicle dynamics and safety. In this paper, we compare two methods for wheel slip control: a loop-shaping Youla parametrization method, and a sliding mode control method. Each controller is designed based on a simple single wheel system. The benefits and drawbacks of both methods are addressed. Finally, the performance and stability robustness of each controller is evaluated based on several metrics in a simulation using a high-fidelity vehicle model with several driving scenarios.
Inclement weather can have a significant impact on surface transportation systems. It can result in hazardous conditions for travelers due to poor visibility, or wet or icy roadways. Weather applications have the potential to provide additional data to surface transportation infrastructure owners and operators, allowing them to better assess the impacts of the weather environment on or around the roadway and to better manage the surface transportation system. Such weather applications can: Collect road weather data from connected vehicles and mobile devices, increasing the number of data sources available. Provide road weather related traveler information to travelers via connected vehicles and devices, such as when and where a hazardous condition exists. Provide the ability to manage road weather response on specific roadways. This SAE Standard specifies interface requirements between vehicles and infrastructure for weather applications, including detailed systems engineering documentation (needs and requirements mapped to appropriate message exchanges). The purpose of this SAE Standard is to enable interoperability supporting these weather applications over a communications technology agnostic interface.
Separate from the event data recorder (EDR), which records and stores data from qualifying vehicle crash events, the Vehicle Control History (VCH) on Toyota vehicles records and stores certain vehicle data based on select driver inputs, such as hard acceleration or braking, or upon the activation of certain vehicle dynamic control systems such as antilock braking system (ABS), traction control (TRAC), vehicle stability control (VSC), and the pre-collision system (PCS). In the United States, VCH was first equipped on the 2013 Toyota RAV4 and has been subsequently introduced into other Toyota and Lexus models. Most recently, in addition to VCH data, additional PCS operational data (PCS-O) and image data (PCS-I) may be recorded and stored. The image storage capability may record under certain conditions such as if the system has automatically applied the vehicle brakes. PCS-O and PCS-I data became available with the launch of Toyota Safety Sense (TSS), a grouping of advanced active safety features equipped on many Toyota vehicles generally available in 2017. Multiple dynamic tests with a 2017 Toyota Corolla were performed that caused the VCH, PCS-O, and PCS-I data to record. Both sets of data were then compared to the test driving sequences. The testing, data, and analysis is presented to illustrate the usefulness of the data in understanding and analyzing certain real-world dynamic events.
ABSTRACT When building simulation models of military vehicles for mobility analysis over deformable terrain, the powertrain details are often ignored. This is of interest for electric and hybrid-electric vehicles where the maximum torque is produced at low speeds. It is easy to end up with the drive wheels spinning and reducing traction and eventually the vehicle digging itself down in the soil. This paper reveals improvements to mobility results using Traction Control Systems for both wheeled and tracked vehicles. Simulations are performed on hard ground and two types of deformable soil, Lethe sand and snow. For each soft soil, simulations have been performed with a simple terramechanics model (ST) based on Bekker-Wong models and complex terramechanics (CT) using the EDEM discrete element soil model which Pratt & Miller Engineering (PME) has been instrumental in developing. To model the traction control system a PD controller is used that tries to limit the slip velocity at low speed and wheel slip at higher velocity. Controlling the slip velocity, i.e. the relative tangential velocity between the wheel and ground, or track and ground is usually best for low speed. A typical preset value would be in the range of 50 – 100 mm/s depending on the usage scenario. Using relative slip velocity also avoids a division by zero at low speeds or at wheel lock-up. The lower value is used mainly for crawl mode, when trying to get unstuck after being dug down deep into the soil. Based on the optimal pre-set values for slip or slip velocity, a correction factor is applied to the throttle to limit the slip or slip velocity. Citation: A. One, A. Two, A. Three, A. Four, A. Five, “Very Really Incredibly Long Example Sample Title”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.
Today’s vehicles rely on multiple interconnected networks of Electronic Control Units (ECUs) that govern almost every automotive function - from engine timing and traction control to side-mirror adjustment and GPS. In-vehicle networks used for inter-ECU communication, most commonly the CAN bus, were not designed with cybersecurity in mind, and as a result, communication by corrupt devices connected to the bus is not authenticated. A multitude of attack vectors allow attackers to control a device on the bus; reports abound of successful hacking of vehicles, by exploiting vulnerable devices and by spoofing messages. Such remote-connectivity and physical-access exploit types must be prevented, to mitigate the threats of impersonation, eavesdropping, replay and reversing. We present the IVAS, In-Vehicle Authentication Scheme. IVAS is an in-place cryptographic scheme: the first CAN messaging solution to ensure both authentication and confidentiality without additional data such as authentication tags. When adequate encryption is used, an adversary’s chances of successfully injecting a spoofed message are equal to the chances for a random message. There is a need for a validation method that deterministically differentiates between random messages and legitimate CAN commands. We take advantage of both static and dynamic redundancy existing in CAN bus traffic, eliminating the need for extra bandwidth. A mathematical proof of the security level of our AE (Authenticated Encryption) scheme is presented, showing that both confidentiality and authenticity are included. No changes to the application code, protocol or chipset are entailed, and runtime key exchange is not required. In addition, any type of serial data bus can be secured by IVAS, so that varied ECUs can work together. The IVAS solution for securing the CAN bus stands out in its ability to authenticate sender integrity and data integrity, blocking malicious messages without adding payloads.
Electronic control units (ECU) from Kawasaki Ninja ZX-6R and ZX-10R motorcycles were tested in order to examine the capabilities and behavior of the event data recorders (EDR). All relevant hexadecimal data was downloaded from the ECU and translated using known and historically proven applications. The hexadecimal translations were then confirmed using data acquisition systems as well as the Kawasaki Diagnostic Software (KDS)1. Numerous tests were performed to establish the algorithms which cause the EDR to record data. Issues of sensor and power loss were analyzed and discussed. Additionally, data sets were studied that involved maximum deceleration from ABS brakes. Similarly, data sets that involved traction control intervention were studied and analyzed. It was determined that the EDR recording ‘trigger’ was caused by the activation of the tip-over sensor, which in turn shuts the engine off. However, specific conditions must be met with regards to the rear wheel rotation prior to engine shut-down. An EDR event was only recorded if the motorcycle was commanded to shut-down by the tip-over sensor, and either had rear wheel movement at the time of shut-down or the rear wheel experienced a certain amount of deceleration in the several seconds prior to shut-down. The ‘time zero’ data element was synchronous with the tip-over commanded shut-down signal. Various data elements were stored at either 10 Hz or 2 Hz for a total of 8 seconds of data prior to the commanded engine shut-down. It was determined that ABS and traction control intervention at the rear wheel could still create a sudden deceleration significant enough to trigger an EDR event after tip-over.
Time for standard naming of safety features Smart cruise control. Intelligent cruise control. Adaptive cruise control. Radar speed control. As The Bard wrote so long ago, “A rose by any other name would smell as sweet.” Sadly that tale did not end well for the protagonists. In today's world of increasingly sophisticated active safety systems, engineers and consumers alike are being bombarded by more and more brand-specific labels for essentially the same technology. Unfortunately, imprecise branding driven more by marketers than technologists threatens to put us all at risk.
In recent times, electric vehicles (EV) are gaining a lot of attention as they run clean and are environment friendly. Recent advances in the applications of integrating control systems in automotive vehicles have made it practicable to accomplish improvement in vehicle's longitudinal and lateral dynamics. This paper deals with a brief overview of current state of art vehicle technologies like direct yaw moment control, traction control and side slip control of EV. There are various controller algorithms available in literature with different torque vectoring strategies. As EV can be precisely controlled because of quick in hub wheel motor response times, therefore various torque vectoring strategies can be comfortably used for enhancing vehicle dynamics. Moreover, by using four independent in-wheel motors, several types of motion controls can be performed. These motion controls are intensively researched by a comprehensive literature review with an aim to obtain desired vehicle handling characteristics. The motivation behind doing this study is to obtain a guideline for systematic development of control strategy. The control law development is discussed in three subsequent stages, namely, Supervisory control, Upper level control and Lower level control. The controller is to be designed and implemented for the torque management of the four independent electric traction motors of a FOX racing electric car.
Vehicle dynamics control (VDC) for motorcycles had a fast growth during the last 10 years. The available technologies comprise curve-safe ABS and traction control (TC) systems, anti-wheelie control, right up to comprehensive motorcycle stability systems including even more control functions. VDC systems rely on real-time information about the current motorcycle dynamic state. Thus motorcycles are equipped with additional sensor units, namely MEMS inertial measurement devices, capable of gathering accelerations and angular rates. The application of model-based estimation theory enables the determination of the necessary information about the in-plane and out-of-plane motion, e.g. the motorcycle lean angle. Since VDC systems include safety critical control functions, the validation within simulations including sensor characteristics is mandatory. The MEMS accelerometer and gyroscope features include low-cost and small footprint, however there are considerable stochastic sensor errors to cope with. In this study the characteristic of different MEMS sensors and their noise models are investigated. The sensor noise terms are identified by analyzing measurement data using the Allan variance method. Different sensors are compared and the stochastic noise coefficients are quantified. The sensor noises are modeled with according random processes defined by linear time-invariant systems and white-noise inputs. As a result, the obtained stochastic sensor models can be used for model-based estimation and control algorithm design, as well as verification within simulation environments.
Since the introduction of electronically controlled air suspension (ECAS) systems in the nineties, no major improvements have been made in the realm of controlling air suspensions in the heavy duty truck market. Despite the lack of improvement, a need exists for intelligently controlled air suspension systems, specifically systems which can be applied to 6x2 axle configurations in the North American market. This study outlines a concept proposal for a novel suspension control concept which encompasses traction control capabilities in addition to suspension control for improved fuel efficiency benefit. The major novelty of the concept is that, by utilizing specific axle configurations and tires, a shift in pressure from the driven to the non-driven axles may result in improvements in the overall fuel economy of the vehicle. The shift in pressure will allow ride height to be maintained while increasing fuel economy benefits if the tires used on the non-driven axles have lower rolling resistances than the tires used on the driven axles. To demonstrate the hypothesized benefits of the system, an estimate of fuel economy was derived through theoretical calculations and known data. Physical testing was conducted to verify the theoretical results. Fuel savings opportunities were identified through the calculated estimates and were further confirmed by full vehicle track tests. Tractors equipped with certain axle configurations (e.g. 6x2 axles) and tires (e.g. trailer tires on the non-driven axle) will experience a noticeable improvement in fuel economy which will ultimately lower fuel costs for operators and reduce the environmental impact of commercial vehicles.
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