Automotive Science and Engineering

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Accurate and correct performance of controller in cruise control systems is important. Hence, in such systems, controller should optimize itself against noise and probable changes in system dynamic. As a matter of fact, in this article three approaches have been conducted to-ward this purpose: MIT, direct estimation and indirect estimation. These approaches are used as controllers to track reference signal. First the performance of each of these three controllers is checked. comparison of performances indicated better behavior for indirect estimation than others. Also, it has less sensitivity against external noise. Finally, by using indirect estimation method as an adaptive control approach, two parallel separate controllers are designed for two inputs, gas and braking, and their performances are compared with recent studies. It shows improvement in performance of adaptive cruise control system to track reference signal.

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The electronic stability control (ESC) system is one of the most important active safety systems in vehicles. Here, we intend to improve the Electronic stability of four in-wheel motor drive electric vehicles. We will design an electronic stability control system based on Type-2 fuzzy logic controller. Since, Type-2 fuzzy controller has uncertainty in input interval furthermore of output fuzziness, it behaves like a robust control, hence it is suitable for control of nonlinear uncertain systems which uncertainty may be due to parameter variation or un-modeled dynamics. The controller output for stabilization of vehicle is corrective yaw moment. Controller output is the torque that distribute by braking and acceleration on both sides of the vehicle. We simulate our designs on MATLAB software. Some drive maneuvers will be carry to validate system performance in vehicle stability maintenance. Simulation results indicate that distributed torque-brake control strategy based on Type-2 fuzzy logic controller can improve the stability and maneuverability of vehicle, significantly in comparison with uncontrolled vehicle and Type-1 fuzzy ESC. Furthermore, we compare the conventional braking ESC with our designed ESC, i.e. distributed exertion of torque ESC and braking ESC in view point of both stabilization and performance. As we will see, proposed ESC can decrease vehicle speed reduction, in addition to better vehicle stability maintenance.

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Hybrid electric vehicles employ a hydraulic braking system and a regenerative braking system together to provide enhanced braking performance and energy regeneration. In this paper an integrated braking system is proposed for an electric hybrid vehicle that include a hydraulic braking system and a regenerative braking system which is functionally connected to an electric traction motor. In the proposed system, four independent anti-lock fuzzy controllers are developed to adjust the hydraulic braking torque in front and rear wheels. Also, an antiskid controller is applied to adjust the regenerative braking torque dynamically.  A supervisory controller, is responsible for the management of this system.  The proposed integrated braking system is simulated in different driving cycles. Fuzzy rules and membership functions are optimized considering the objective functions as SoC and slip coefficient in various road conditions. The simulation results show that the fuel consumption and the energy loss in the braking is reduced. In the other hand, this energy is regenerated and stored in the batteries, especially in the urban cycles with high start/stop frequency. The slip ratio remains close to the desired value and the slip will not occur in the whole driving cycle. Therefore, the proposed integrated braking system can be considered as a safe, anti-lock and regenerative braking system.
 

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In order to lowering level of emissions of internal combustion engines (ICEs), they should be optimally controlled. However, ICEs operate under numerous operating conditions, which in turn makes it difficult to design controller for such nonlinear systems. In this article, a generalized unique controller for idle speed control under whole loading conditions is designed. In the current study, instead of tedious time-consuming trial-and-error based methods, soft computing techniques are employed to tune a proportional-integral-derivative (PID) controller which controls idle speed of engine. Since model based design technique is employed, a mean value model (MVM) is taken advantage due to its evidenced merits. Moreover, a brief introduction to the selected meta-heuristics is given followed by a flowchart to show how the engine model is linked to the optimization algorithms. A set point of 750 rpm is fed to the system, and the weighted sum of the three characteristics of mean squared error, control energy, and percent overshoot of the control system is set to the problem objective function to be minimized. It is evidenced that of all the examined meta-heuristics, Bees Algorithm (BA) converges to a better solution. Finally, to consider the effectiveness of the developed optimal controllers in disturbance rejection, they are implemented to the engine MVM model. The results of the research indicate, all the four optimally designed control systems, albeit the intermediate superiority, are of conspicuous success in compensating for the input disturbances of the load torque.

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In cruise control systems, the performance of the controller is important. Hence, in order to have accurate results, the nonlinear behavior of a vehicle model should also be considered. In this article, a vehicle with a nonlinear model is controlled by using a nonlinear method. The nonlinear term of the model is the generated torque of engine, which is a polynomial equation. In addition, feedback linearization is used as a nonlinear method in order to design two parallel controllers to control the movement of the vehicle. These two parallel controllers are used to control braking and gas pedals which are in charge of the angular velocity of the wheels. To check the performances of controllers, first, each controller is used separately. Finally, two parallel controllers are used to track the reference signal. Comparison between results shows that the designed controller is able to reduce the convergence time of about 10 seconds. This improvement is near 35% in comparison with near studies. In addition, it can reduce the error between the velocity of the vehicle and the values of the reference signal that results in more safety for passengers.

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    Modeling and identification of the system of Iranian cars is one of the most basic needs of automotive and consumer groups and has a broad role for safe driving. It has happened with speed increasing or changing of shift gear, effects on water temperature or the car's torque has been observed, but how much and how intensely and with what algorithm this effect is identifiable, can be modeled and controlled, because up to now an algorithm that can show these effects during driving has not existed that what reaction should be made by the vehicle when it occurs untimely.
    Identification of each automobile sector lonely has been considered in recent decades, and in some cases, some relationships have been investigated, but from a control point of view, the lack of comprehensive effects of all parts of a car on the other parts is to get an identification algorithm in the automotive industry, and it requires more in-depth studies, because the complexity of the behavior of different parts of the car has made many attempts not fully understandable. Hear it's supposed to control different parameters of Iranian vehicles by using LS estimation and fuzzy logic controller and the simulation is done in Matlab software by storing and validating data of a Dena vehicle through CAN network.

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In this paper an idea for hybridization of conventional vehicles has proposed. The case study performed on one of the common vehicles on country roads i.e. Samand. This vehicle has high production volume but low fuel performance therefore hybridization of it could be attractive for its manufacture. This paper aims that the hybridization idea and its structure to need minimum mechanical modifications. In consequence attractiveness of this idea for industry could be high. A cost optimization has been performed for sizing of additional components such as electric motors and battery modules and the simulation results has been adopted to verify the proposed idea for case study with hybrid simulation of GT-Suit and MATLAB softwares.      

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The control of Antilock Braking Systems (ABS) is a difficult problem, because of their nonlinearities and uncertainties appearing in their dynamics and parameters. To overcome these issues, this paper proposes a new adaptive controller for the next generation of ABS. After considering a complex vehicle dynamic, a triple adaptive fuzzy control system is presented. Important parameters of the vehicle dynamic include two separated brake torques for front ands rear wheels, as well as longitudinal weight transfer which is caused by the acceleration or deceleration. Because of the nonlinearity of the vehicle dynamic model, three fuzzy-estimators have been suggested to eliminate nonlinear terms of the front and rear wheels’ dynamic. Since the vehicle model parameters change due to variations of road conditions, an adaptive law of the controller is derived based on Lyapunov theory to adapt the fuzzy-estimators and stabilize the entire system. The performance of the proposed controller is evaluated by some simulations on the ABS system. The results demonstrate the effectiveness of the proposed method for ABS under different road conditions.

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Due to the increasing level of air pollution and the reduction of fossil fuels, the need for new technologies and alternative fuels is felt more than ever. Proton exchange membrane fuel cells (PEMFCs) are one of these technologies, which have been of great interest to the researchers due to the benefits of non-contamination, high efficiency, fast start-up, and high power density. The proper functioning of the fuel cell requires thermal management and water management within the cells. To this end, in this work, the effect of different parameters on the performance of PEM fuel cell was investigated. The results demonstrated that the performance of the cell increases with increasing the pressure in the low current densities, while in the high current density, performance decreases with increasing the pressure of the cell. Also, the study of the effect of relative humidity shows that increasing the relative humidity of the cathode does not have much effect on the performance of the cell while increasing the relative humidity of the anode improves the performance of the cell.

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Traffic control is a major and common problem in large-scale urban decision-making, particularly in metropolises. Several models of intelligent highways have been proposed to tackle the issue, and the longitudinal speed control of vehicles remains a key issue in the field of intelligent highways. Many researchers have been investigating the longitudinal speed control of vehicles. However, their proposed models disregard important and influential presumptions. In the present study, the longitudinal dynamics control of vehicles in the presence of nonlinear factors, such as air resistance, rolling resistance, a not ideal gearbox, an internal combustion engine and a torque converter, is investigated. Moreover, considering the presented model and using model reference adaptive control, a proper controller is designed to control the longitudinal speed of intelligent vehicles. The results of the proposed model, which is validated by commercial software, are in good agreement with real-world situations. Hence, a positive step is taken for controlling longitudinal speed of intelligent vehicles on an intelligent highway platform.

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Rollover of sport utility vehicles is a critical challenge for dynamic stability of the vehicle. Due to the high rate of fatalities resulted from the rollover, in order to reduces the injuries, the design of active vehicle controllers has received significant attention among the researchers and car companies. In this article, a multi-criteria optimum method is discussed in order to design a dynamics stabilizing controller via differential braking with an optimum braking torque distribution. To this end, the nonlinear control method on the basis of the sliding mode techniques has been implemented that provides ride comfort, improve safety performance, and maintain maneuverability. To address the trade-off between the conflicting requirements of vehicle dynamic control in terms of maneuverability and rollover prevention capability, we formulate an artificial intelligence-based multi-criteria genetic algorithms. The simulation verification analysis indicates that the utilized optimum distribution braking torques result in the desired enhancement in roll stability of the vehicle.

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Reducing the fuel consumption and energy use in transportation systems are the active research areas in recent years. This paper considers the repetitive mission of the intercity passenger buses as a case for fuel reduction. A look-ahead energy management system is proposed which uses the information about the geometry and speed limits of the road ahead. This data can be extracted using road slope and speed limits database in combination with a GPS unit. A fuzzy gain scheduling algorithm is proposed to improve the performance of the look-ahead control. The road slope and speed limit specifications called road pattern can define some two dimensional regions. The main parameters of the proposed fuzzy look-ahead controller are optimized in each region using the genetic algorithm.  The final output of the proposed controller is the desired speed that regularly is fed to the conventional cruise controller with new set points. The simulation results of the proposed energy management system show that the fuel consumption is significantly reduced.

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In this paper, an adaptive cruise control system is designed that is controlled by a neural network model. This neural network model is trained with data resulting from the simulation of a multi-objective nonlinear predictive adaptive cruise control system. For this purpose, first, an adaptive cruise control system was designed using the concept of model predictive control based on a nonlinear model to maintain the desired speed of the driver, maintain a safe distance with the car in front, reducing fuel consumption and increasing ride comfort. Due to the time-consuming computations in predictive control systems and the consequent need for powerful and expensive hardware, it was decided to use the extracted data from the simulation of this designed cruise control system to train a neural network model and use this model to achieve control objectives instead of the predictive controller. Using the neural network model in the cruise control system, despite a significant reduction in computation time, the control objectives were well achieved, and in fact a combination of model predictive controller accuracy and neural network controller speed was used.

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This paper experimentally investigates the trafficability of a small tracked vehicle on a slope. An increase in the angle of slope inclination may divert the vehicle from its path. In other words, the deviation of the vehicle is due to a sudden increase in the yaw angle. Also, the tip-over occurs at a specific slope angle. The locomotion of the small tracked vehicle on soils with different terramechanics (such as cohesion, internal friction angle, cohesive modulus, and friction modulus) is also simulated to evaluate its slope-traversing performance. Moreover, the impact of velocity and soil type on traversing a slope is measured. The proposed yaw angle control system is modeled for controlling the yaw angle of the tracked vehicle. This controller is designed through co-simulation. It keeps the tracked vehicle at zero yaw angle to achieve straight locomotion on slopes. It is compared to the PI, PID, and fuzzy controllers. The response of this controller is faster than PI and PID controllers. A Comparison between fuzzy and proposed yaw angle controller yields almost similar responses. The mechanism of the proposed yaw angle controller is also easier to understand. The precision of the controller's performance is measured by simulating over different terrains.

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In this paper, firstly chaotic behavior of the lateral dynamics of vehicle is investigated by the use of numerical tools including Lyapunov exponent and bifurcation diagrams. To this end rout to chaos along with period doubling and quasi-periodic responses are demonstrated in terms of bifurcation diagrams. After chaos analysis, a novel controller commensurate with the chaotic characteristics of the system, in conformity with Poincaré map is represented to suppress the chaotic behavior of lateral movement. The Poincaré map of the system is derived by means of a neuro fuzzy network. A robust Fuzzy system on the basis of nonlinear Ott-Grebogi-Yorke (OGY) method forms the control system. Closed-loop results of the system shows effectiveness of the chaos controller in extreme conditions.

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Environmental pollution and reduction of fossil fuel resources can be considered as the most important challenges for human society in the recent years. The results of previous studies show that the main consumer of fossil fuels and, consequently, most of the air pollutants, is related to the transportation industry and especially cars. The increasing growth of vehicles, the increase in traffic and the decrease in the average speed of inner-city vehicles have led to a sharp increase in fuel consumption. To address this problem, automakers have proposed the development and commercialization of hybrid vehicles as an alternative to internal combustion vehicles. In this paper, the design of an energy management system in a fuel-cell hybrid vehicle based on the look-ahead fuzzy control is considered. The preparation of fuzzy rules and the design of membership functions is based on the fuel efficiency curve of the fuel-cell. In look-ahead fuzzy control, the ahead conditions of the vehicle are the basis for decision in terms of slope and speed limit due to path curves as well as battery charge level. The fuzzy controller will determine the on or off status of the fuel-cell, as well as the power required. The motion of the fuel-cell hybrid vehicle on a real road is simulated and the performance of the proposed look-ahead controller is compared with the base controller (thermostatic method). The simulation results show that using the proposed approach can reduce the fuel consumption of the fuel-cell hybrid vehicle as well as travel time.

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In this paper, analysis and control of the chaotic vibrations in bounce dynamic of vehicle have been studied according to the comparison of controller based on the nonlinear control and chaos controller on the basis of the chaotic system properties. After modeling the vehicle dynamic, the chaotic behavior of the uncontrolled system was determined using combination of the numerical analysis including bifurcation diagrams and max Lyapunov exponent. The system parameters values were then identified in the quasi-periodic and chaotic behavior system. In order to eliminate the chaotic vibrations, the control signal was first developed using a nonlinear fast-terminal sliding mode control algorithm that its control gains are estimated online by fuzzy logic which was designed for vehicle vertical dynamics. Then the delayed feedback control was designed based on the development of Pyragas algorithm to control the system based on the properties of the chaotic system and generation of a small control signal. Comparison of the feedback system depicts priority of the Fuzzy-Pyragas controller in less energy consumption and better behavior.

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Electric vehicles are attaining significant attention recently and the current legislation is forcing the automotive industry to electrify the productions. Regardless of electric energy accumulation technology, drive technology is one of the vital components of EVs. The motor drive technology has been mainly developed based on the application which required position/velocity control. In automotive application, however, torque control is an important aspect since the drivers have already used to drive the vehicle based on torque control approach in traditional powertrain system. In this article, a model-based approach is employed to develop a controller which can guarantee the precise control of the induction motors torque for a micro electric vehicle (EV) application regardless of operating conditions. The implementation of the control drive was conducted in MATLAB/Simulink environment, followed by Model In the Loop simulation and testing at various test conditions to confirm the robustness of the developed drive. Direct Torque Control (DTC) with optimum voltage vector selection method is employed to control the motor torque that requires fewer power electronics to process its operation and hence lowers the cost of implementation. The result shows the practicality of the designed control system and its ability to track reference torque commands. Vitally, the controlled approach shows fair abilities to control IMs to produce torque at both the motoring and regenerative modes which is a highly important requirement in electrical propulsion powertrains. Furthermore, the controller’s response time was within the industrial standard range which confirms its suitability for industrial implementation at low cost.