Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
Air-fuel ratio (AFR) control is important for the exhaust emission reduction while using the\nthree-way catalytic converter in the spark ignition (SI) engine. However, the transient cylinder air\nmass is unable to acquire by sensors directly and it may limit the accuracy of AFR control. The complex\nengine dynamics and working conditions make the intake air estimation a challenge work. In this\npaper, a novelty design of intake air observer is investigated for the port-injected SI engine. The intake\nair dynamical modeling and the parameter fitting have been carried out in detail. Extended Kalman\nFilter (EKF) has been used to optimize the instantaneous cylinder charge estimation and minimize\nthe effort of pump gas fluctuation, random noise, and measurement noise. The experiment validation\nhas been conducted to verify the effectiveness of the proposed method....
The traditional predictive method cannot fully reflect the complex nonlinear characteristics and regularities of automobile and\nparts sales data, so the prediction precision is not high. Thepurpose of this paper is to propose the gray GM(1,1) nonlinear periodic\npredictive model by introducing the seasonal variation index to improve predictive accuracy of the single GM(1,1) model. Firstly,\nthe paper analyzes concept of GM(1,1) and then proposes the gray GM(1,1) nonlinear periodic predictive model to forecast\nautomobile parts sales. Themodel algorithm used gray theory and accumulated technology to generate new data and set up unified\ndifferential equations to find the fitting curve of automobile parts sales prediction by the seasonal variation index to remove\nrandom elements. Lastly, the gray GM(1,1) nonlinear periodic predictive model is used for empirical analysis; the result of\nexample shows that the model proposed in the paper is feasible. The superiority of the proposed predictive model compared with\nthe single gray GM(1,1) model is demonstrated. The reliability of this model is experienced by the accuracy test, which provides a\ntheoretical guidance for the prediction of automobile part sales. And the average relative error is reduced by 8.52% compared with\nthe single GM(1,1) model....
To improve the efficiency of electric vehicles (EVs), a planetary two-speed transmission is\nproposed, which consists of a brushless direct current (BLDC) motor, a turbo-worm reducer, two\nmulti-disc wet brakes, and a Simpson planetary gearset. Based on the devised electronic actuator\nfor shifting, the rotation direction of the BLDC shaft determines the gear ratio of the transmission.\nFor acquiring smooth shift, the state-space equations with control variables of transmission are\nderived, and a three-stage algorithm is suggested. During the brake engagement process, the\noptimal control strategy has been developed using linear quadratic regulator control, considering\nthe jerk and friction work of the brake. The simulation results show that the proposed optimal\ncontrol strategy could reduce the slipping friction work of the brake and improve the shifting\nquality of EVs. The optimal control trajectory of the BLDC motor was conducted on the electronic\nshifting actuator bench test....
A growing literature suggests that widespread travel conducted through driverless\nconnected and automated vehicles (CAVs) accessed as a service, in contrast to those personally\nowned, could have significant impacts on the sustainability of urban transportation. However, it is\nunclear how the general public currently considers willingness to travel in driverless vehicles, and\nif they would be more comfortable doing so in one personally owned or one accessed as a service.\nTo address this, we collected travel survey data by intercepting respondents on discretionary or social\ntrips to four popular destinations in a medium-size U.S. city in the spring of 2017. After collecting\ndata on how the respondent reached the survey site and the tripâ??s origin and destination, survey\nadministrators then asked if respondents would have been willing to make their current trip in either a\npersonally-owned driverless vehicle or through a driverless vehicle service. Over one-third expressed\nwillingness to use both forms, while 31% were unwilling to use either. For those that considered only\none, slightly more favored the personally-owned model. Consideration of an existing mobility service\nwas consistently a positive and significant predictor of those that expressed willingness to travel in a\ndriverless vehicle, while traveling downtown negatively and significantly influenced consideration\nof at least one form of driverless vehicle. These findings highlight the diverse public views about the\nprospect of integration of CAVs in transportation systems and raise questions about the assumption\nthat travelers to central city locations would be early adopters of automated vehicle mobility services....
When a safety-related fault in the motor controller is detected, the torque output of the motor cannot be effectively shut off in time\nand an overcurrent occurs at the moment of switching. The advantages and disadvantages of the open circuit and active shortcircuit\nmethods are analyzed. Combining the advantages of these two operations, this paper proposes a new mixed voltage\nmodulation method. It introduces a voltage modulation ratio that represents the duty cycle of the open circuit operation during a\nPWM period. This ratio is first set to a fixed value and gradually reduced to zero. The inverter is switched at a mixed operation and\nfinally remains in the active short-circuit mode. The current can be quickly converged by a freewheeling diode of open circuit.\nAfter switching to active circuit, the brake torque is safety. The effectiveness of this shutoff method was verified by simulations and\nexperiments. It shows that current fluctuations are suppressed and the torque output is also within a safety range. In addition, this\nshutoff method does not require any additional sensor information and is simple to implement....
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