Showing papers in "International Journal of Automotive Technology in 2018"

Design of Regenerative Anti-Lock Braking System Controller for 4 In-Wheel-Motor Drive Electric Vehicle with Road Surface Estimation

Abstract: This paper presents a regenerative anti-lock braking system control method with road detection capability. The aim of the proposed methodology is to improve electric vehicle safety and energy economy during braking maneuvers. Vehicle body longitudinal deceleration is used to estimate a road surface. Based on the estimation results, the controller generates an appropriate braking torque to keep an optimal for various road surfaces wheel slip and to regenerate for a given motor the maximum possible amount of energy during vehicle deceleration. A fuzzy logic controller is applied to fulfill the task. The control method is tested on a four in-wheel-motor drive sport utility electric vehicle model. The model is constructed and parametrized according to the specifications provided by the vehicle manufacturer. The simulation results conducted on different road surfaces, including dry, wet and icy, are introduced.

Design and Evaluation of Robust Cooperative Adaptive Cruise Control Systems in Parameter Space

Abstract: This paper is on the design of cooperative adaptive cruise control systems for automated driving of platoons of vehicles in the longitudinal direction. Longitudinal models of vehicles with simple dynamics, an uncertain first order time constant and vehicle to vehicle communication with a communication delay are used in the vehicle modeling. A robust parameter space approach is developed and applied to the design of the cooperative adaptive cruise control system. D-stability is chosen as the robust performance goal and the feedback PD controller is designed in controller parameter space to achieve this D-stability goal for a range of possible longitudinal dynamics time constants and different values of time gap. Preceding vehicle acceleration is sent to the ego vehicle using vehicle to vehicle communication and a feedforward controller is used in this inter-vehicle loop to improve performance. Simulation results of an eight vehicle platoon of heterogeneous vehicles are presented and evaluated to demonstrate the efficiency of the proposed design method. Also, the proposed method is compared with a benchmark controller and the feedback only controller. Time gap regulation and string stability are used to assess performance and the effect of the vehicle to vehicle communication frequency on control system performance is also investigated.

On the experimental analysis of integral sliding modes for yaw rate and sideslip control of an electric vehicle with multiple motors

Abstract: With the advent of electric vehicles with multiple motors, the steady-state and transient cornering responses can be designed based on high-level reference targets, and implemented through the continuous torque control of the individual wheels, i.e., torque-vectoring or direct yaw moment control. The literature includes several papers describing the application of the sliding mode control theory to torque-vectoring. However, the experimental implementations of sliding mode controllers on real vehicle prototypes are very limited at the moment. More importantly, to the knowledge of the authors, there is lack of experimental assessments of the performance benefits of direct yaw moment control based on sliding modes, with respect to other controllers, such as the proportional integral derivative controllers or linear quadratic regulators currently used for stability control in production vehicles. This paper aims to reduce this gap by presenting an integral sliding mode controller for concurrent yaw rate and sideslip control. A new driving mode, the Enhanced Sport mode, is proposed, inducing sustained high values of sideslip angle, which can be safely limited to a specified threshold. The system is experimentally assessed on a four-wheel-drive electric vehicle along a wide range of maneuvers. The performance of the integral sliding mode controller is compared with that of a linear quadratic regulator during step steer tests. The results show that the integral sliding mode controller brings a significant enhancement of the tracking performance and yaw damping with respect to the more conventional linear quadratic regulator based on an augmented single-track vehicle model formulation.

Review of Secure Communication Approaches for In-Vehicle Network

Abstract: In the connected vehicles, connecting interfaces bring threats to the vehicles and they can be hacked to impact the vehicles and drivers Compared with traditional vehicles, connected vehicles require more information transfer Sensor signals and critical data must be protected to ensure the cyber security of connected vehicles The communications among ECUs, sensors, and gateways are connected by in-vehicle networks This paper discussed the state-of-art techniques about secure communication for in-vehicle networks First, the related concepts in automotive secure communication have been provided Then we have compared and contrasted existing approaches for secure communication We have analyzed the advantages/disadvantages of MAC and digital signatures for message authentication and compared the performance and limitations of different cryptographic algorithms Firewall and intrusion detection system are introduced to protect the networks The constraints and features of different intrusion detection approaches are presented After that, the technical requirements for cryptographic mechanism and intrusion detection policy are concluded Based on the review of current researches, the future development directions of the automotive network security have been discussed The purpose of this paper is to review current techniques on automotive secure communication and suggest suitable secure approaches to implement on the in-vehicle networks

Development of a self-driving car that can handle the adverse weather

Abstract: Lane and road recognition are essential for self-driving where GPS solution is inaccurate due to the signal block or multipath in an urban environment. Vision based lane or road recognition algorithms have been studied extensively, but they are not robust to changes in weather or illumination due to the characteristic of the sensor. Lidar is a sensor for measuring distance, but it also contains intensity information. The road mark on the road is made to look good with headlight at night by using a special paint with good reflection on the light. With this feature, road marking can be detected with lidar even in the case of changes in illumination due to the rain or shadow. In this paper, we propose equipping autonomous cars with sensor fusion algorithms intended to operate in a different weather conditions. The proposed algorithm was applied to the self-driving car EureCar (KAIST) in order to test its feasibility for real-time use.

Test Scenario Design for Intelligent Driving System Ensuring Coverage and Effectiveness

Abstract: Intelligent vehicle greatly benefits traffic safety, efficiency and driving comfortable. With the development of intelligent driving technology and its application, it is becoming increasingly important to do effective and comprehensive tests before putting on the market. Comprehensively considering the cost of test, an automatic generation method of test scenarios is proposed to ensure both coverage and effectiveness. Based on the analyzed key infuence factors of an intelligent driving system, the analytic hierarchy process (AHP) is used to determine their importance and accordingly an complex index is defined, based on which an improved test case generation algorithm based on the pairwise independent combinatorial testing tool (PICT) is proposed to ensuring both combinational coverage and complexity of test cases. Finally, the test scenario is generated by clustering these discrete test cases considering similarity and complexity. The high complex test cases are preferred to be combined together and conducted preferentially to increase the test efficiency. The effectiveness of this method is validated by applying it on a lane departure warning system (LDW).

PID Controller Modelling and Optimization in Cr Systems with Standard and Reduced Accumulators

Abstract: The proportional-integrative-derivative (PID) controller and the pressure control valve of a Common Rail system are modelled by taking into account electronic, electrical, hydraulic and mechanical aspects A fully predictive model of the injection apparatus is realized and validated by means of comparison with experimental data The effects of the PID parameters on the injection system dynamics are illustrated and discussed on the basis of model results, which refer to steadystate and transient working conditions The influence of the accumulator size on the rail pressure time history is investigated when the rail volume is dramatically reduced (up to 25 cm3) In particular, the effect of the large rail pressure drop that occurs at the end of the main injection for Minirail layout solutions is examined when after injections are implemented An objective is to try to determine possible suitable values of the PID controller parameters and of the pressure-sensor sampling-frequency for rails of reduced size

Influence of temperature on the tyre rolling resistance

Abstract: Temperature is a very important factor controlling rolling resistance of road vehicle tyres. There are at least three different temperatures that may be considered as important factors controlling thermal conditions of the rolling tyre. The most common measure of the thermal conditions during tyre rolling is ambient air temperature. The other two are: pavement temperature and “tyre” temperature. Tyre temperature is the most difficult to establish, as temperatures of different parts of rolling tyres differ considerably, thus there is a problem to obtain representative values. In the authors’ opinion, air temperature is the most universal and reliable parameter to measure. The article presents results obtained in the Technical University of Gdansk during laboratory and road measurements of different car tyres rolling on different pavements. The knowledge of rolling resistance characteristics is important for modelling car dynamics as well as fuel consumption. It is also necessary to establish proper test conditions in the future standardized on-road method of measuring rolling resistance. The results indicate that generally each tyre and pavement combination is influenced by the air temperature in a unique way, but at the same it is possible to propose some general influence factors that may be used to normalize measurements to the standard temperature of 25 °C.

From Offline to Adaptive Online Energy Management Strategy of Hybrid Vehicle Using Pontryagin’s Minimum Principle

Abstract: This paper details the development of an energy management strategy (EMS) for real-time control of a multi hybrid plug-in electric bus. The energy management problem has been formulated as an optimal control problem in order to minimize the fuel consumption of the bus drivetrain for a typical day of operation. Considering the physical characteristics of the studied hybrid electric bus and its well-known daily tour, the Pontryagin’s minimum principle (PMP) is firstly used as the mean to obtain offline optimal EMS. Afterward, in order to adapt the proposed strategy for real-time implementation, the proposed control parameters are adapted online using feedback from the battery state of energy (SOE) which allows us to accurately control the battery SOE in the presence of wide range of uncertainties. The work proposed in this paper is conducted on a dedicated high-fidelity dynamical model of the hybrid bus, that was developed on MATLAB/TruckMaker software. The performance evaluation of the proposed strategy is carried out using a normalized driving cycles to represent different driving scenarios. Obtained results show that among the investigated methods, it is reasonable to conclude that the proposed adaptive online strategy based on PMP is the most suitable to design the targeted EMS.

Hierarchical Direct Yaw-Moment Control System Design for In-Wheel Motor Driven Electric Vehicle

Abstract: In this study, a hierarchical structured direct yaw-moment control (DYC) system, which consists of a main-loop controller and a servo-loop controller, is designed to enhance the handling and stability of an in-wheel motor driven driven electric vehicle (IEV). In the main loop, a Fractional Order PID (FO-PID) controller is proposed to generate desired external yaw moment. A modified Differential Evolution (M-DE) algorithm is adopted to optimize the controller parameters. In the servo-loop controller, the desired external yaw moment is optimally distributed to individual wheel torques by using sequential quadratic programming (SQP) approach, with the tire force boundaries estimated by Unscented Kalman Filter (UKF) based on a fitted empirical tire model. The IEV prototype is virtually modelled by using Adams/Car collaborating with SolidWorks, validated by track tests, and serves as the control plant for simulation. The feasibility and effectiveness of the designed control system are examined by simulations in typical handling maneuver scenarios.

Autonomous Emergency Braking Considering Road Slope and Friction Coefficient

Abstract: The Autonomous Emergency Braking (AEB) systems have been actively studied for the safety enhancement and commercialized for the past few years. Because the driver tends to overly rely upon active safety systems, AEB needs to be designed to reflect the real road situations such as various road slope and friction coefficient. In this study, an AEB control algorithm is proposed to compensate for the effects of the slope and the friction of road. Based on the maximum possible deceleration for the real road conditions, the minimum braking distance is described with margin parameters for AEB activation control. The deceleration controller with a feedforward term is designed to avoid the collision during AEB operation on real road conditions. The proposed algorithm is validated in simulations first and the experimental verification is performed in the various slope conditions.

System power loss optimization of electric vehicle driven by front and rear induction motors

Abstract: Power loss optimization aiming at the high-efficiency drive of front-and-rear-induction-motor-drive electric vehicle (FRIMDEV) as an effective way to improve energy efficiency and extend driving range is of high importance. Different from the traditional look-up table method of motor efficiency, power loss optimization of the dual- motor system based on the loss mechanism of induction motor (IM) is proposed. First of all, based on the power loss characteristic of FRIMDEV from battery to wheels, the torque distribution optimization model aiming at the minimum system power loss is put forward. Secondly, referring to d-q axis equivalent model of IM, the power loss functions of the dual-IM system are modeled. Then, the optimal torque distribution coefficient (β o) between the two IMs is derived, and the theoretical switching condition (T sw) between the single- and dual-motor-drive mode (SMDM and DMDM) is confirmed. Finally, a dual-motor test platform is developed. The derived torque distribution strategy is verified. The influence of motor temperature on β o and T sw are tested, and the correction models based on temperature difference are proposed. Based on the system power loss analysis, it can be confirmed that, under low load conditions, the SMDM takes priority over the DMDM, and the controller of the idling motor should be shut down to avoid the additional excitation loss. While under middle to high load conditions, even torque distribution (β o = 0.5) is preferred if the temperature difference between the two IMs is small; otherwise, β o should be corrected based on dual-motor temperatures. The theoretical T sw derived without dealing with temperature difference is a function only of motor speed, while temperature difference correction of it should be conducted in actual operations based on motor resistance changing with temperature.

Dynamics Analysis of Torsional Vibration Induced by Clutch and Gear Set in Automatic Transmission

Abstract: The torsional vibration generated during clutch engagement directly affects the shifting quality of automatic transmissions, where the noise source stems from both the clutch and the gear set. To predict the dynamical response and driveline oscillation, a comprehensive mathematical model of the vehicle powertrain equipped with automatic transmission is developed with consideration of nonlinearities in the clutch and the planetary gear set. For the clutch, the dynamics of stickslip is described for the transition between the slipping to locked states. The gear backlash model is used to analyze the rattle noise of the planetary gear set. Based on extensive powertrain simulations for the clutch engagement process, the magnitude of vibration propagation in the driveline are predicted to identify the primary factors of noise generation.

New Optimal Power Management Strategy for Series Plug-In Hybrid Electric Vehicles

Abstract: Recently Plug-in hybrid electric vehicles (PHEVs) have gained increasing attention due to their ability to reduce the fuel consumption and emissions. In this paper a new efficient power management strategy is proposed for a series PHEV. According to the battery state of charge (SOC) and vehicle power requirement, a new rule-based optimal power controller with four different operating modes is designed to improve the fuel economy of the vehicle. Furthermore, the teaching-learning based optimization (TLBO) method is employed to find the optimal engine power and battery power under the specified driving cycle while the fuel consumption is considered as the fitness function. In order to demonstrate the effectiveness of the proposed method, four different driving cycles with various numbers of driving distances for each driving cycle are selected for the simulation study. The performance of the proposed optimal power management strategy is compared with the rule-based power management method. The results verify that the proposed power management method could significantly improve the fuel economy of the series PHEV for different driving conditions.

Effect of operating parameters on diesel/propane dual fuel premixed compression ignition in a diesel engine

Abstract: In this research, the effects of three operating parameters (Diesel injection timing, propane ratio, and exhaust gas recirculation (EGR) rates) in a diesel-propane dual fuel combustion were investigated. The characteristics of dual-fuel combustion were analyzed by engine parameters, such as emission levels (Nitrogen oxides (NOx) and particulate matter (PM)), gross indicated thermal efficiency (GIE) and gross IMEP Coefficient of Variance (CoV). Based on the results, improving operating strategies of the four main operating points were conducted for dual-fuel PCCI combustion with restrictions on the emissions and the maximum pressure rise rate. The NOx emission was restricted to below 0.21 g/kWh in terms of the indicated specific NOx (ISNOx), PM was restricted to under 0.2 FSN, and the maximum pressure rise rate (MPRR) was restricted to 10 bar/deg. Dual-fuel PCI combustion can be available with low NOx, PM emission and the maximum pressure rise rate in relatively low load condition. However, exceeding of PM and MPRR regulation was occurred in high load condition, therefore, design of optimal piston shape for early diesel injection and modification of hardware optimizing for dual-fuel combustion should be taken into consideration.

Wet Single Clutch Engagement Behaviors in the Dual-Clutch Transmission System

Abstract: A frictional torque was generated by a lubricated slip contact between a wet clutch pad and a steel separator during a wet clutch engagement. It is necessary to understand the generation of frictional torque to improve the performance of the frictional torque transfer and the durability of the wet clutch system. The analytical modeling of wet clutch torque transfer considers the effects of surface roughness, permeability, the elastic modulus of the frictional material, lubricant viscosity, temperature, etc. Experimental apparatus for wet clutch engagement was designed for the measurement of frictional torque transfer during wet clutch engagement. The experimental results were compared with the analytical results under various operational conditions for the verification of the theoretical analysis to evaluate the performance of the wet clutch system. Some correlations were investigated between the experimental and analytical results. We found that computation by analytical modeling can predict the effects of oil temperature, applied force, and slip speed, as well as engagement period and frictional torque transfer shapes.

Energy Consumption Analysis of Different Bev Powertrain Topologies by Design Optimization

Abstract: Flexible layout of electric motors in battery electric vehicles (BEVs) has enabled different powertrain topologies to be used. However, these different powertrain topologies also affect the overall efficiency of energy conversion from the electrochemical form stored in the battery to the mechanical form on the driving wheels for vehicle propulsion. In this study, a methodology combining an energy-based BEV simulation model with the genetic algorithm optimization approach is applied to evaluate the energy efficiency of three different BEV powertrain topologies. The analysis is carried out assuming two different urban driving conditions, as exemplified by the New European Drive Cycle (NEDC) and the Japanese JC08 drive cycle. Each of the three BEV powertrain topologies is then optimized – in terms of its electric motor size and, where applicable, gear reduction ratio – for minimum energy consumption. The results show that among the three powertrain topologies, the wheel-hub drive without gear reducers consumes the least energy. The energy consumption of BEVs under the more aggressive JC08 drive cycle is consistently 8 % above that under NEDC for all three powertrain topologies considered.

Model Predictive Coordinated Control for Dual-Mode Power-Split Hybrid Electric Vehicle

Abstract: Power-split hybrid electric vehicles (HEVs) have great potential fuel efficiency and have attracted extensive research attention with regard to their control system. The coordinated controller in HEV plays an important role in tracking the optimal state reference generated by the energy management strategy (EMS), so as to reach the desired fuel efficiency. Meanwhile, the coordinated controller also has a significant impact on driving performance. To improve its performance, the design of a model predictive control (MPC) based coordinated controller in power-split HEV is presented. First, a non-linear, time-varying constrained control oriented transmission model of a dual-mode power-split HEV is formulated to describe this control problem. Then, to solve this problem, the non-linear part in the transmission model is linearised, and a linear MPC is used to obtain the control signals for the motors and engine at each time step. To meet the requirements of real-time computation, a fast MPC method is also applied to reduce the online computation effort. Simulations and experiments demonstrate the effectiveness of the proposed MPC-based coordinated controller.

Gaussian process regression feedforward controller for diesel engine airpath

Abstract: Gaussian Process Regression (GPR) provides emerging modeling opportunities for diesel engine control. Recent serial production hardwares increase online calculation capabilities of the engine control units. This paper presents a GPR modeling for feedforward part of the diesel engine airpath controller. A variable geotmetry turbine (VGT) and an exhaust gas recirculation (EGR) valve outer loop controllers are developed. The GPR feedforward models are trained with a series of mapping data with physically related inputs instead of speed and torque utilized in conventional control schemes. A physical model-free and calibratable controller structure is proposed for hardware flexibility. Furthermore, a discrete time sliding mode controller (SMC) is utilized as a feedback controller. Feedforward modeling and the subsequent airpath controller (SMC+GPR) are implemented on the physical diesel engine model and the performance of the proposed controller is compared with a conventional PID controller with table based feedforward.

Effects of Residual Ash on Dpf Capture and Regeneration

Abstract: To study the effects of residual ash on the capture and regeneration of a diesel particulate filter (DPF), repeated capture and complete regeneration experiments were conducted. An engine exhaust particulate sizer was used to measure the particle size distribution of diesel in the front and back of DPF. Discrepancies in the size distribution of the particulate matter in repeated trapping tests were analyzed. To achieve complete DPF regeneration, a DPF regeneration system using nonthermal plasma technology was established. The regeneration carbon removal mass and peak temperatures of DPF internal measuring points were monitored to evaluate the effect of regeneration. The mechanism explaining the influence of residual ash on DPF capture and regeneration was thoroughly investigated. Results indicate that the DPF trapping efficiencies of the nuclear-mode particles and ultrafine particles have significant improvements with the increase quantity of residual ash, from 90 % and 96.01 % to 94.17 % and 97.27 %, respectively. The exhaust backpressure of the DPF rises from 9.41 kPa to 11.24 kPa. Heat transfer in the DPF is improved with ash, and the peak temperatures of the measuring points accordingly increase. By comparing the regeneration trials, the elapsed time for complete regeneration and time difference for reaching the peak temperature between adjacent reaction interfaces are extended with increased quantity of ash. The carbon removal mass rises by 34.00 %.

Path Tracking for Autonomous Race Car Based on G-G Diagram

Abstract: Currently, the autonomous driving technique is attracting increasing research focus from all over the world. Generally, the control systems of an autonomous vehicle include environment perception, path planning and path tracking control systems. In this paper, the path tracking control issue of the autonomous vehicle will be focused on. Few of the previous proposed path tracking controllers consider the vehicle driving and handling limit, which degenerates the potential of the autonomous vehicle to finish the desired path as quick as possible. To this end, this paper proposes a path tracking controller for autonomous vehicle, which aims at pushing it to the driving and handling limit. The limit dynamic performance of the autonomous vehicle is represented by the G-G diagram, which indicates the acceleration capability of the autonomous vehicle. The G-G diagram is obtained by phase portrait method and it is validated by a modified FSAE racing car. Finally, a path tracking controller based on the G-G diagram is proposed. The simulation validation results demonstrate the effectiveness of the proposed controller.

Adaptive feedforward control of a steer-by-wire system by online parameter estimator

Abstract: The tracking control of the steer-by-wire (SBW) system to achevie desired steering motion is the core issue for the design of algorithm Most of model-based tracking control assumed the constant parameters without the consideration of dynamic characteristics The external disturbances and model nonlinearities can bring uncertainties of the system parameters To reduce the influence of parameter uncertainties, an online estimator by output error identification method is proposed to estimate the dynamic parameters of a SBW system Meanwhile, the parameter gradient projection method is applied to eliminate the parameter drift, while a full order state observer is developed to weaken the effects of noise disturbance during the parameter identification Since the sensitivity of parameter uncertainties for the feedforward control, the online estimator is incorporated into the control model and improve the controlled robustness The proposed adaptive feedforward controller is conducted by the real-time experiments to show the tracking performance

Transient Dynamic Characteristics of a Non-Pneumatic Mechanical Elastic Wheel Rolling Over a Ditch

Abstract: The transient dynamic characteristic of a tire, which has a significant effect on vehicle handling stability and ride comfort, is difficult to study in detail because of its highly non-linear behavior. In this study, the transient dynamic characteristics of a non-pneumatic wheel, called the mechanical elastic wheel (MEW), which was rolling over a ditch were investigated by the explicit dynamic finite element (FE) method. A three-dimensional FE model of MEW considering geometric nonlinearity, material nonlinearity and large contact deformation between the wheel and the road, was established. For the validation of the accuracy and reliability of the FE model of MEW, the simulation and the experimental results of the radial stiffness and footprint of MEW were compared and analyzed. A dynamic simulation of the validated FE model of MEW rolling over a ditch was conducted using the ABAQUS/Explicit program. The equivalent stress and the contact stress generated during the process of the rolling MEW impacting the ditch were studied in detail. The effect of the rolling speed on the transient dynamic characteristics was also analyzed based on the simulation results. The simulation results could provide guidance for the optimization of the MEW structure and vehicle dynamics.

Detection of Cognitive and Visual Distraction Using Radial Basis Probabilistic Neural Networks

Abstract: This paper suggests a real-time method for detecting a driver’s cognitive and visual distraction using lateral driving performance measures The algorithm adopts radial basis probabilistic neural networks (RBPNNs) to construct classification models In this study, combinations of two driving performance data measures, including the standard deviation of lane position (SDLP) and steering wheel reversal rate (SRR), were considered as measures of distraction Data for training and testing the RBPNN models were collected under simulated conditions in which fifteen participants drove on a highway While driving, they were asked to complete auditory recall tasks or arrow search tasks to create cognitively or visually distracted driving periods As a result, the best performing model could detect distraction with an average accuracy of 780 %, which is a relatively high accuracy in the human factors domain The results demonstrated that the RBPNN model using SDLP and SRR could be an effective distraction detector with easy-to-obtain and inexpensive inputs

Real-Time Longitudinal Location Estimation of Vehicle Center of Gravity

Abstract: The longitudinal location of a vehicle’s center of gravity (CG) is used as an important parameter for vehicle safety control systems, and can change considerably according to various driving conditions. Accordingly, for the better performance of vehicle safety control systems, it is essential to obtain the accurate CG location. However, it is generally difficult to acquire the value of this parameter directly through sensors due to cost reasons. In this study, a practical algorithm for estimating vehicle’s longitudinal CG location in real time is proposed. This algorithm is derived based only on longitudinal motion of the vehicle, excluding excessive lateral, yaw and roll movements of the vehicle. Moreover, the proposed algorithm has main differences from previous studies in that it does not require information such as vehicle mass, vehicle moments of inertia, road grade or tire-road surface friction, which are difficult to acquire. In the proposed algorithm, the relationship between the ratio of rear-to-front tire longitudinal force and the corresponding wheel slips are used to determine the CG location. To demonstrate a practical use of the proposed algorithm, the ideal brake force distribution is tested. The proposed CG estimation algorithm and its practical use are verified via simulations and experiments using a test vehicle equipped with electro-mechanical brakes in the rear wheels. It is shown that the estimated CG locations are close to the actual ones, and that the deceleration can be maximized by the ideal brake force distribution.

Tire wear estimation based on nonlinear lateral dynamic of multi-axle steering vehicle

Abstract: This paper presents a novel nonlinear dynamic model of a multi-axle steering vehicle to estimate the lateral wear amount of tires Firstly, a 3DOF nonlinear vehicle dynamic model is developed, including dynamic models of the hydropneumatic suspension, tire, steering system and toe angle The tire lateral wear model is then built and integrated into the developed vehicle model Based on the comparison of experimental and simulation results, the nonlinear model is proved to be better than a linear model for the tire wear calculation In addition, the effects of different initial toe angles on tire wear are analyzed As simulation results shown, the impact of the dynamic toe angle on the tire wear is significant The tire wear amount will be much larger than that caused by normal wear if the initial toe angle increases to 1° - 15° The results also suggest that the proposed nonlinear model is of great importance in the design and optimazation of vehicle parameters in order to reduce the tire wear

Characterizing Driver Stress Using Physiological and Operational Data from Real-World Electric Vehicle Driving Experiment

Abstract: Electric Vehicle (EV) is becoming a viable and popular option, but the acceptance of the technology can be challenging and lead to an elevated driving stress The existing studies on stress of vehicle driving has been mainly limited to the non-EVs or survey analysis In this research, EV driving data of 40 subjects is analyzed, where each subject was asked to drive an EV over a 53 km course in a suburban city of South Korea Physiological data including electroencephalogram (EEG) and eye-gazing were obtained along with vehicle operational data such as state of charge, altitude, and speed The dataset was rich in information, but individual difference and nonlinear patterns made it extremely difficult to draw meaningful insights As a solution, an information-theoretic framework is proposed to evaluate mutual information between physiological and operational data as well as the entropy of physiological data itself The result shows two groups of subjects, one not showing much evidence of stress and the other exhibiting sufficient stress Among the subjects who showed sufficient driving stress, 9 out of the top 10 high EEG-entropy drivers were female, one driver showed a strong pattern of range anxiety, and several showed patterns of uphill climbing anxiety

Reliability of Nanofluid Concentration on the Heat Transfer Augmentation in Engine Radiator

Abstract: Nanofluids, the fluid suspensions of nanomaterial, became a promising fluid that is invoked when heat transfer increase is required. Using of nanofluids as a coolant in the engine radiators is a crucial topic for the thermal engines manufactrers due to the expected enhancement in the cooling process. In this study, Two nanofluids (Al2O3/water and CuO/water) flowing in a flat tube of radiator are investigated numerically to evaluate thermal and flow performance. The resizing process for the radiator is performed by using nanofluid instead of water flow. A significant reduction in the radiator volume is achieved due to marked improvement in the heat transfer performance while, the required pumping power after this reduction in the volume is increased over that needed for base fluid. The normalized heat transfer (heat transfer to the pumping power) is found to be a function of both Reynolds number and nanofluid concentration ratio while the ratio of the normalized heat transfer is found to be dependent only on the nanofluid concentration ratio. These dependencies are formulated as general correlations.

Development of preview active suspension control system and performance limit analysis by trajectory optimization

Abstract: The main role of the suspension system is to achieve ride comfort by reducing vibrations generated by the road roughness. The active damper is getting much attention due to its reduced cost and ability to enhance ride comfort especially when the road ahead is measurable by an environment sensor. In this study a preview active suspension control system was developed in order to improve ride comfort when the vehicle is passing over a speed bump. The control system consists of a feedback controller based on the skyhook logic and a feedforward controller for canceling out the road disturbance. The performance limit for the active suspension control system was computed via trajectory optimization to provide a measure against which to compare and validate the performance of the developed controller. The simulation results indicated that the controller of this study could enhance ride comfort significantly over the active suspension control system employing only the skyhook feedback control logic. Also the developed controller, by displaying similar control pattern as the trajectory optimization during significant time portions, proved that its control policy is legitimate.

Development of a Driving Behavior-Based Collision Warning System Using a Neural Network

Abstract: An advanced driver assistance system (ADAS) uses radar, visual information, and laser sensors to calculate variables representing driving conditions, such as time-to-collision (TTC) and time headway (THW), and to determine collision risk using empirically set thresholds. However, the empirically set threshold can generate differences in performance that are detected by the driver. It is appropriate to quickly relay collision risk to drivers whose response speed to dangerous situations is relatively slow and who drive defensively. However, for drivers whose response speed is relatively fast and who drive actively, it may be better not to provide a warning if they are aware of the collision risk in advance, because giving collision warnings too frequently can lower the reliability of the warnings and cause dissatisfaction in the driver, or promote disregard. To solve this problem, this study proposes a collision warning system (CWS) based on an individual driver’s driving behavior. In particular, a driver behavior model was created using an artificial neural network learning algorithm so that the collision risk could be determined according to the driving characteristics of the driver. Finally, the driver behavior model was learned using actual vehicle driving data and the applicability of the proposed CWS was verified through simulation.