Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 5 Articles
In conventional steering system, a feedback torque is produced fromthe contact between tire and road surface and its flows through\nmechanical column shaft directly to driver. This allows the driver to sense the steering feel during driving.However, in steer by wire\n(SBW) system, the elimination of the mechanical column shaft requires the system to generate the feedback torque which should\nproduce similar performance with conventional steering system. Therefore, this paper proposes a control algorithm to create the\nforce feedback torque for SBW system. The direct current measurement approach is used to estimate torque at the steering wheel\nand front axle motor as elements to the feedback torque, while, adding the compensation torque for a realistic feedback torque. The\ngain scheduling with a linear quadratic regulator controller is used to control the feedback torque and to vary a steering feel gain.\nTo investigate the effectiveness of the proposed algorithm, a real-time hardware in the loop (HIL) methodology is developed using\nMatlab XPC target toolbox. The results show that the proposed algorithm is able to generate the feedback torque similar to EPS\nsteering system. Furthermore, the compensation torque is able to improve the steering feel and stabilize the system....
The HEV electric motor is typically powered by a battery\npack through power electronics. The HEV battery is\nrecharged either by the engine or from regenerative braking.\nThe electric drive mode is very limited for an HEV due to\nthe limited battery power. A more powerful battery will\nincrease the electric drive range of the vehicle, thus\nimproving fuel economy. However, there will be a need to\nrecharge the battery using an electric outlet since the\nregenerative braking and limited engine usage will not be\nsufficient to fully recharge the larger battery pack. In this\npaper, fuzzy logic energy management strategy for a Plug-in\nHybrid Electric Vehicle (PHEV) is presented. Since large\namount of electric energy is stored in the battery from the\nelectric power grid, the fuel consumption is reduced\nsignificantly as compared with HEV counterpart. The\nproposed energy management strategy is implemented on a\nPHEV model in ADVISOR and the model is then simulated\nfor several number of drive cycles. The proposed PHEV\nalgorithm results are compared with the determinacy rulebased\nenergy management strategy for HEV with similar\nbattery capacity as PHEV....
The paper provides a simple parking path programming\nstrategy for automatic parking system (APS). The control\nstrategy employs the minimum turning radius of the vehicle\nby means of a distance infrared sensor to determine the\nparking path. The programming strategy can simplify the\nanalysis of the parking path; therefore, there is no need to\napply any expensive sensors and complex mathematical\ncalculation to determine the parking path. In the experiment,\na four- wheel model vehicle has been tested in combination\nwith an microprocessor (ARM 9) and a distance infrared\nsensor. A vehicle could be safely and correctly parked in the\nparking space by following these routes....
This paper utilizes a linear two-degree-of-freedom vehicle model to calculate the nominal value of the vehicle�s nondrive-wheel\nspeed difference and investigates methods of estimating the yaw acceleration and sideslip angular speed. A vehicular dynamic\nstability control system utilizing this nondrive-wheel speed difference is then developed, which can effectively improve a vehicle�s\ndynamic stability at a very low cost. Vehicle cornering processes on roads of different frictions and with different vehicle speeds are\nexplored via simulation, with speed control being applied when vehicle speed is high enough to make the vehicle unstable. Driving\nsimulator tests of vehicle cornering capacity on roads of different friction coefficients are also conducted....
This manuscript discusses the development of the thermal\ncomfort zones, during summer and winter periods, inside\nvehicular cabins. This is done using two thermal modeling\napproaches; specifically Berkeley and Fanger computations.\nThe limiting boundaries of the thermal comfort zone when\ncomputed by the Berkeley model is determined by the\nOverall thermal Sensation (OS �± 0.5), while according to\nFanger model, the zone is determined by the Predicted Mean\nVote index (PMV �± 0.5). The Berkeley simulation uses a\nvirtual thermal manikin to predict the thermal sensation and\ncomfort inside the cabin under different environmental\nconditions, while maintaining the cabin homogeneous state\nover a relative humidity range of (20-60%). The manikin\nclothing reflects the summer period through; short sleeve\nwith long trousers at an approximate clothing insulation\nvalue of 0.5 clo. Additionally, the winter clothing for winter\nis long thick sleeve, long thick trousers, hand-wear and\nfootwear with approximate clothing insulation value of 1 clo.\nThe metabolic rate for a human passenger is set at 1.4 met to\nrepresent a seated human activity level. The same conditions\nare also used for the Fanger model except the range of\nrelative humidity, which is (20-80%). The results show that\nthe lower and upper temperature limits for the summer\ncomfort window are at standard conditions of 22.4 and\n27.3�°C for the Berkeley model and at 23.1 and 27.4 �°C for the\nFanger model. On the other hand, the temperature limits for\nthe winter comfort window are at 19.8 and 25.2�°C for the\nBerkeley model and at 18.6 and 24.6 �°C for the Fanger model.\nAdditionally, the proposed study conducted a sensitivity\nanalysis of these windows by changing (increase/decrease)\nof the metabolism, the cabin air velocity, and the clothing\ninsulation values....
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