Multi-parameter predictive shift schedule of automatic mechanical transmission for electric bus

2021 ◽  
pp. 095440702110506
Author(s):  
Jiankun Peng ◽  
Hailong Zhang ◽  
Haonan Li ◽  
Yuanguang Jiang ◽  
Zhanjiang Li
Keyword(s):  
Electricity Consumption ◽  
Mechanical Transmission ◽  
Multiple Parameters ◽  
Shift Schedule ◽  
Electric Bus ◽  
Study Results ◽  
Artificial Neural Network Ann ◽  
Short Horizon ◽  
Relationship Of ◽  
Road Slope

Shift schedule is crucial in improving the dynamic and economic performance of electric vehicles (EVs) equipped with automatic mechanical transmission (AMT). As the driver, vehicle, and road constitute a closed-loop inseparable system, identifying the states of both vehicle and road is fundamental to realizing optimal shift schedule. However, the existing shift strategies neglect the coupling relationship of multiple parameters to the shift strategy. To minimize this gap, this paper presents a novel multi-parameter shift schedule based on model predictive control. Firstly, cubature Kalman filters (CKF) algorithm is employed to accurately estimate vehicle quality and road slope, which could improve the energy economy of EVs. Secondly, an artificial neural network (ANN) is adopted to forecast the compound future short horizon driving conditions, which contains the perdition information of vehicle velocity and road slope. Meanwhile, the AMT predictive shift schedule based on the above estimated and forecast information is constructed, which used dynamic programming to optimize in the rolling horizon. Simulation study results indicate that the ANN-based predictive approach shows better performance on accuracy and robustness than that of Markov chain, and the electricity consumption over China typical urban driving cycle (CTUDC) is further reduced by 6.79% than that of multi-parameter rule-based shift schedule.

Modeling and Analysis of Three-Parameter Power Shift Schedule for Pure Electric Bus of AMT

2012 ◽  
Vol 155-156 ◽  
pp. 648-652
Author(s):  
Li Peng Luo ◽  
Jun Qiang Xi ◽  
Xing Long Liu ◽  
Yu Hui Hu
Keyword(s):  
Dynamic Performance ◽  
Mechanical Transmission ◽  
Vehicle Speed ◽  
Vehicle Model ◽  
Slope Surface ◽  
Modeling And Analysis ◽  
Shift Schedule ◽  
Power Shift ◽  
Electric Bus ◽  
Two Parameter

The design of shift schedule is of great importance for the drivability of vehicular automatic mechanical transmission. Currently, the most widely used is two-parameter shift schedule, but it ignores the influence of dynamic conditions and ramp conditions. In this paper, a three-parameter power shift schedule based on vehicle speed, acceleration and throttle angle has been proposed, which makes the vehicle have the best performance in all kinds of slope surface. Then the vehicle model is established by Cruise and Matlab/simulink. The simulation results show that the three-parameter power shift schedule can effectively enhance the vehicle dynamic performance.

scholarly journals Three-parameter Shift Schedule of Automatic Mechanical Transmission for Electric Bus

2018 ◽  
Vol 145 ◽  
pp. 504-509 ◽  
Author(s):  
Haonan Li ◽  
Hongwen He ◽  
Jiankun Peng ◽  
Zhanjiang Li
Keyword(s):  
Mechanical Transmission ◽  
Shift Schedule ◽  
Electric Bus

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sunil K. Deokar ◽  
Nachiket A. Gokhale ◽  
Sachin A. Mandavgane
Keyword(s):  
Three Dimensional ◽  
Current Approach ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Operational Parameters ◽  
Initial Concentration ◽  
Combined Application ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
Biomass Ashes

Abstract Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

scholarly journals Relationship of mammographic densities to breast cancer risk

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Engy A. Ali ◽  
Mariam Raafat
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Statistical Significance ◽  
Significant Risk ◽  
Close Relation ◽  
Mammographic Screening ◽  
Mammographic Breast Density ◽  
Study Results ◽  
Relationship Of ◽  
Mammographic Densities

Abstract Background Our goal was to find out the relation between mammographic densities and cancer of the breast according to the recent ACR classification. From the medical records of Kasereliny Hospital, 49,409 women were subjected to digital mammography for screening, of which 1500 breast cancer cases were collected. The mammographic categories of breast density were ACR-A, B, C, and D, which were detected by two senior radiologists. All radiological classifications were made using both standard mammographic views bilaterally. Two-sided tests of statistical significance were represented by all the P values. Results From 2014 to 2019, 49,409 women came for digital mammographic screening, their age ranges between 40 and 65, and all of them are included in the study. One thousand cases of breast cancer cases were radiologically and pathologically diagnosed. Different densities were arranged in descending pattern depending on the frequency of positive cases: D (13.7%), C (3.3%), B (2.7%), A (2.2%). There is positive significant risk ratio among every higher mammographic density in comparison to the lower density. Conclusion Our study results show that the risk of breast cancer is in close relation to the mammographic breast density.

Shut-in Stabilization Time Optimization for Better Reservoir Pressure Monitoring Harnessing Neural Network Modeling

2021 ◽  
Author(s):  
Mohammad Al Kadem ◽  
Ali Al Ssafwany ◽  
Ahmed Abdulghani ◽  
Hussain Al Nasir
Keyword(s):  
Neural Network ◽  
Neural Network Modeling ◽  
Stabilization Time ◽  
Pressure Derivative ◽  
Absolute Relative Error ◽  
Pressure Sensitive ◽  
Time Optimization ◽  
Average Absolute Relative Error ◽  
Study Results ◽  
Artificial Neural Network Ann

Abstract Stabilization time is an essential key for pressure measurement accuracy. Obtaining representative pressure points in build-up tests for pressure-sensitive reservoirs is driven by optimizing stabilization time. An artificial intelligence technique was used in the study for testing pressure-sensitive reservoirs using measuring gauges. The stabilization time function of reservoir characteristics is generally calculated using the diffusivity equation where rock and fluid properties are honored. The artificial neural network (ANN) technique will be used to predict the stabilization time and optimize it using readily available and known inputs or parameters. The values obtained from the formula known as the diffusion formula and the ANN technique are then compared against the actual values measured from pressure gauges in the reservoirs. The optimization of the number of datasets required to be fed to the network to allow for coverage over the whole range is essential as opposed to the clustering of the datasets. A total of about 3000 pressure derivative samples from the wells were used in the testing, training, and validation of the ANN. The datasets are optimized by dividing them into three fractional parts, and the number optimized through monitoring the ANN performance. The optimization of the stabilization time is essential and leads to the improvement of the ANN learning process. The sensitivity analysis proves that the use of the formula and ANN technique, compared to actual datasets, is better since, in the formula and ANN technique, the time was optimized with an average absolute relative error of 3.67%. The results are near the same, especially when the ANN technique undergoes testing using known and easily available parameters. Time optimization is essential since discreet points or datasets in the ANN technique and formula would not work, allowing ANN to work in situations of optimization. The study was expected to provide additional data and information, considering that stabilization time is essential in obtaining the pressure map representation. ANN is a superior technique and, through its superiority, allows for proper optimization of time as a parameter. Thus it can predict reservoir log data almost accurately. The method used in the study shows the importance of optimizing pressure stabilization time through reduction. The study results can, therefore, be applied in reservoir testing to achieve optimal results.

scholarly journals Simulating the Stress-Strain Relationship of Geomaterials by Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongbo Zhao ◽  
Zenghui Huang ◽  
Zhengsheng Zou
Keyword(s):  
Support Vector Machine ◽  
Constitutive Relationship ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Ann Model ◽  
Stress Strain ◽  
Svm Model ◽  
Strain Relationship ◽  
Artificial Neural Network Ann ◽  
Relationship Of

Stress-strain relationship of geomaterials is important to numerical analysis in geotechnical engineering. It is difficult to be represented by conventional constitutive model accurately. Artificial neural network (ANN) has been proposed as a more effective approach to represent this complex and nonlinear relationship, but ANN itself still has some limitations that restrict the applicability of the method. In this paper, an alternative method, support vector machine (SVM), is proposed to simulate this type of complex constitutive relationship. The SVM model can overcome the limitations of ANN model while still processing the advantages over the traditional model. The application examples show that it is an effective and accurate modeling approach for stress-strain relationship representation for geomaterials.

scholarly journals Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China

2020 ◽  
Vol 12 (17) ◽  
pp. 7188
Author(s):  
Jiankun Peng ◽  
Jiwan Jiang ◽  
Fan Ding ◽  
Huachun Tan
Keyword(s):  
Transfer Matrix ◽  
Electricity Consumption ◽  
Original Data ◽  
Performance Measure ◽  
Driving Cycle ◽  
Statistical Characteristics ◽  
Hybrid Electric Bus ◽  
Navigation Function ◽  
Electric Bus ◽  
Hybrid Electric

A driving cycle is important to accomplish an accurate depiction of a vehicle’s driving characteristics as the traction motor’s flexible response to stop and start commands. In this paper, the driving cycle construction of an urban hybrid electric bus (HEB) in Zhengzhou, China is developed in which a measurement system integrating global positioning and inertial navigation function is used to acquire driving data. The collected data are then divided into acceleration, deceleration, uniform, and stop fragments. Meanwhile, the velocity fragments are classified into seven state clusters according to their average velocities. A transfer matrix applied to reveal the transfer relationship of velocity clusters can be obtained with statistical analysis. In the third stage, a three-part construction method of driving cycle is designed. Firstly, according to the theory of Markov chain, all the alternative parts that satisfy the construction’s precondition are selected based on the transfer matrix and Monte Carlo method. The Zhengzhou urban driving cycle (ZZUDC) could be determined by comparing the performance measure (PM) values subsequently. Eventually, the method and the cycle are validated by the high correlation coefficient (0.9972) with original data of ZZUDC than that of the other driving cycle (0.9746) constructed with traditional micro-trip and as well by comparing several statistical characteristics of ZZUDC and seven international cycles. Particularly, with around 20.5 L/100 km fuel and approximately 12.8 kwh/100 km electricity consumption, there is a narrow gap between the energy consumption of ZZUDC and WVUCITY, and their characteristics are similar.

scholarly journals Effects of Internal Service Quality on Work Engagement: A Case Study of Hotel Soaltee Crowne Plaza

2020 ◽  
Vol 5 (2) ◽  
pp. 63-76
Author(s):  
Eveeta Shakya ◽  
Puja Tamang
Keyword(s):  
Service Quality ◽  
Work Engagement ◽  
Significant Relationship ◽  
Research Work ◽  
Independent Variables ◽  
Study Results ◽  
Internal Service Quality ◽  
Relationship Of ◽  
The Impact ◽  
Employee Work Engagement

This study examined the Service Quality (SERVQUAL) model with the Internal Service Quality (ISQ) dimensions such as tangibles, reliability, responsiveness, assurance and empathy that impact on employee work engagement. This research work was conducted in Hotel Soaltee Crowne Plaza (SCP), one of the 5-star deluxe hotels of Nepal. The study has adopted an inferential research design to meet the study objectives regarding the impact of independent variables on work engagement of SCP Hotel. A structured questionnaire was distributed to 126 sample respondents out of a total of 503 employees of SCP hotel. The study reveals a significant relationship of work engagement with empathy and not with rest of the independent variables. Based on the findings it is recommended that employees should be taken good care of, and employers should be well aware of the employees’ emotional level since empathy has shown a highly significant relationship with work engagement. Study results have scope of future reference whereby implementing SERVQUAL dimensions for employee work engagement and reduction in employee turnover and improved.

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