PID plus fuzzy logic method for torque control in traction control system

A Traction Control System (TCS) is used to control the driving force of an engine to prevent excessive slip when a vehicle starts suddenly or accelerates. The torque control strategy determines the driving performance of the vehicle under various drive-slip conditions. This paper presents a new torque control method for various drive-slip conditions involving abrupt changes in the road friction. This method is based on a PID plus fuzzy logic controller for driving torque regulation, which consists of a PID controller and a fuzzy logic controller. The PID controller is the fundamental component that calculates the elementary torque for traction control. In addition, the fuzzy logic controller is the compensating component that compensates for the abrupt change in the road friction. The simulation results and the experimental vehicle tests have validated that the proposed controller is effective and robust. Compared with conventional PID controllers, the driving performance under the proposed controller is greatly improved.     

A novel adaptive algorithm with an IIR filter and a variable step size for active noise control in a short duct

The intake system in an automotive engine has a short duct compared with that of the exhaust system. The filtered-x LMS (FX-LMS) algorithm has been applied to the active noise control (ANC) system in a short acoustic duct. This algorithm design is based on the FIR (finite impulse response) filter; however, it has a slow convergence issue due to a large number of zero coefficients. To improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct because the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filtered-u LMS algorithm (FU-LMS) based on the infinite impulse response (IIR) is developed to consider backward acoustic propagation. Generally, this algorithm has a stability problem. The stability issue was improved using an error-smoothing filter. In this paper, the recursive LMS algorithm with a variable step size and smoothing error filter is designed. This recursive LMS algorithm, the FU-VSSLMS algorithm, uses an IIR filter. With fast convergence and good stability, this algorithm is suitable for the ANC system in a short acoustic duct, such as the intake system of an automotive engine. This algorithm is applied to the ANC system of a short acoustic duct. The disturbance signals used as primary noise source are a sinusoidal signal embedded in white noise and the chirp signal, which has a variable instantaneous frequency. The test results demonstrate that the FU-VSSLMS algorithm has a superior convergence performance when compared with the FX-LMS and FX-LMS algorithms. The algorithm can be successfully applied to the ANC system in a short duct, such as the intake duct.     

Adaptive neural network based fuzzy control for a smart idle stop and go vehicle control system

Idle stop and go (ISG) is a low cost but very effective technology to improve fuel efficiency and reduce engine emissions by preventing unnecessary engine idling. In this study, a new method is developed to improve the performance of conventional ISG by monitoring traffic conditions. To estimate frontal traffic conditions, an ultra-sonic ranging sensor is employed. Several fuzzy logic algorithms are developed to determine whether the engine idling is on or off. The algorithms are evaluated experimentally using various data gathered in real areas with traffic congestion. The evaluation results show that the method developed can reduce the chance of false application of ISG significantly while improving fuel efficiency up to 15%.     

Robust design optimization of suspension system by using target cascading method

This study presents the robust design optimization process of suspension system for improving vehicle dynamic performance (ride comfort, handling stability). The proposed design method is so called target cascading method where the design target of the system is cascaded from a vehicle level to a suspension system level. To formalize the proposed method in the view of design process, the design problem structure of suspension system is defined as a (hierarchical) multilevel design optimization, and the design problem for each level is solved using the robust design optimization technique based on a meta-model. Then, In order to verify the proposed design concept, it designed suspension system. For the vehicle level, 44 random variables with 3% of coefficient of variance (COV) were selected and the proposed design process solved the problem by using only 88 exact analyses that included 49 analyses for the initial meta-model and 39 analyses for SAO. For the suspension level, 54 random variables with 10% of COV were selected and the optimal designs solved the problem by using only 168 exact analyses for the front suspension system. Furthermore, 73 random variables with 10% of COV were selected and optimal designs solved the problem by using only 252 exact analyses for the rear suspension system. In order to compare the vehicle dynamic performance between the optimal design model and the initial design model, the ride comfort and the handling stability was analyzed and found to be improved by 16% and by 37%, respectively. This result proves that the suggested design method of suspension system is effective and systematic.     

Integration of a single cylinder engine model and a boost system model for efficient numerical mapping of engine performance and fuel consumption

A numerical engine mapping methodology is proposed for the engine performance and fuel consumption map generation. An integrated model is developed by coupling a single cylinder GT-Power® engine model with a MATLAB/ Simulink® based boost system model to simulate a turbocharged diesel engine over the entire engine operating speed and load ranges within reasonable computational constraints. A single cylinder engine model with the built-in multi-zone combustion modeling option in GT-Power® is configured as a predictive engine model. The cycle averaged simulation result from the engine model is used as the boundary conditions of the boost system including intake and exhaust manifolds and a turbocharger. The boost system model developed in MATLAB/Simulink® platform calculates the intake and exhaust conditions which are fed back to the engine model. The integrated system model predicts the performance and fuel consumption of a turbocharged diesel engine with better predictive capability than mean value engine models. Its computational time is fast enough to simulate the engine over the entire engine operation range compared to multi-cylinder engine models.     

SCR control strategy based on ANNs and Fuzzy PID in a heavy-duty diesel engine

The paper presents an innovative method combining artificial neural networks (ANNs) with Fuzzy PID to demonstrate the advantages of this control approach for meeting both NOx emission requirements and NH3 slip targets. An ANN model was utilized to simulate the formation of NOx emissions under various engine operating conditions. Next, an effective closed-loop control strategy with a type of feedback known as fuzzy PID is adopted for on-line, real-time control of 32.5% aqueous urea dosing in the exhaust stream. The new strategy explores the benefits by simulation and testing in the environments of Matlab/Simulink and ESC/ETC, respectively. The notable achievement of considerable NOx reduction and an acceptably small NH3 slip is obtained based on this new, feasible and effective strategy.     

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

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.     

Comparative investigation on the aerodynamic effects of combined use of underbody drag reduction devices applied to real sedan

To reduce the aerodynamic drag, the performance of the underbody aerodynamic drag reduction devices was evaluated based on the actual shape of a sedan-type vehicle. An undercover, under-fin, and side air dam were used as the underbody aerodynamic drag reduction devices. In addition, the effects of the interactions based on the combination of the aerodynamic drag reduction devices were investigated. A commercial sedan-type vehicle was selected as a reference model and its shape was modeled in detail. Aerodynamic drag was analyzed by computational fluid dynamics at a general driving speed on highway of 120 km/h. The undercover reduced the slipstream area through the attenuation of the longitudinal vortex pair by enhancing the up-wash of underflow, thereby reducing the aerodynamic drag by 8.4 %. The under-fin and side air dam showed no reduction in aerodynamic drag when they were solely attached to the actual complex shape of the underbody. Simple aggregation of the effects of aerodynamic drag reduction by the individual device did not provide the accurate performance of the combined aerodynamic drag reduction devices. An additional aerodynamic drag reduction of 2.1 % on average was obtained compared to the expected drag reduction, which was due to the synergy effect of the combination.     

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

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.