scholarly journals Online Optimal Energy Distribution of Composite Power Vehicles Based on BP Neural Network Velocity Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qingjian Jiang ◽  
Zhijun Fu ◽  
Qiang Hu
Keyword(s):  
Neural Network ◽  
Energy Distribution ◽  
Bp Neural Network ◽  
Power Distribution ◽  
Vehicle Speed ◽  
Distribution Method ◽  
Power Sources ◽  
Optimal Energy ◽  
Velocity Prediction ◽  
Rolling Optimization

In this paper, an online optimal energy distribution method is proposed for composite power vehicles using BP neural network velocity prediction. Firstly, the predicted vehicle speed in the future period is obtained via the output of a BP neural network, where the current vehicle driving state and elapsed vehicle speed information is used as the input. Then, according to the predicted vehicle speed, an energy management method based on model predictive control is proposed, and online real-time power distribution is carried out through rolling optimization and feedback correction. Cosimulation results under urban drive cycle show that the proposed method can effectively improve the energy efficiency of composite power sources compared with the commonly used method with the assumption of prior known driving conditions.

Angular Velocity Prediction of GFSINS Based on BP Neural Network

2012 ◽  
Vol 452-453 ◽  
pp. 846-852
Author(s):  
Hai Qing Duan ◽  
Qi Dan Zhu
Keyword(s):  
Neural Network ◽  
Angular Velocity ◽  
Real Time ◽  
Bp Neural Network ◽  
Neural Cells ◽  
Bp Network ◽  
Velocity Prediction ◽  
Sample Interval ◽  
Neural Network Algorithm ◽  
Configuration Scheme

Aiming at low precision for traditional angular velocity algorithms in GFSINS, a BP neural network algorithm without complex mathematic computation is put forward to calculate angular velocity. Based on a ten-accelerometer configuration scheme, the accelerometer output, sample interval and fixed position are chosen as input, angular velocity got by lognormal algorithm is chosen as output, and 5000 sample data is trained in the several conditions with different hiding layers, neural cells and training steps. Then a three-layer BP network model with 30 hiding layer neural cells is built. Finally, the angular velocity is predicted in real time by the model. Results show that network has strong adaptive capability and real time, and compared with lognormal algorithm, prediction time is almost equal, but prediction precision of angular velocity is nearly improved by three times.

Prediction of Blasting Vibration Velocity by BP Neural Network

2014 ◽  
Vol 539 ◽  
pp. 736-740
Author(s):  
Guang Zhang ◽  
Yi Wen Yang ◽  
Zhi Jun Liu ◽  
Jing Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Measured Data ◽  
Accurate Prediction ◽  
Vibration Velocity ◽  
Blasting Vibration ◽  
Model Based ◽  
Velocity Prediction

In order to ensure the success of the blasting projects, accurate prediction of blasting vibration is necessary. However, blasting vibration is affected by different blasting conditions. The paper analyzed the impacts of the conditions to vibration, and built a blasting vibration velocity prediction model based on BP neural network. Comparing the predicted results with measured data, there has good correlation between them; it can be well applied to predict blasting vibration velocity.

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