TY - JOUR
T1 - 基于摩擦纳米发电机的车辆踏板运动量化模型
AU - Zhang, Haodong
AU - Wang, Wuhong
AU - Lu, Xiao
AU - Tan, Haiqiu
AU - Jiang, Xiaobei
AU - Shi, Jian
N1 - Publisher Copyright:
© 2022 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.
PY - 2022/9
Y1 - 2022/9
N2 - Aiming at the problem of complex structure, dependence on complex materials and external power supply of vehicle pedal angle sensor. A quantization model of pedal movement based on the fan-shaped sector-single electrode sliding mode triboelectric nanogenerator (S-SETENG) is proposed. First, on the basis of the single electrode sliding mode triboelectric nanogenerator (SETENG), according to the potential law between the contact area and the open circuit voltage value, the S-SETENG that can obtain pedal movement information is developed. Secondly, through the simulated driving experiment, the pedal movement data in the natural driving state is obtained, including the pedal angle data output by the driving simulator and the voltage data output by S-SETENG. Then, using S-SETENG voltage data and vehicle pedal angle data to complete the training of pedal movement quantification model. Finally, according to the results of the prediction models on the test set, the acceleration pedal movement quantization model based on gate recurrent unit (GRU) and the brake pedal movement quantization model based on long-short term memory (LSTM) perform best, and the value of R square(R2) exceeds 0.94, which proves the accuracy and feasibility of this method. This not only presents a new principle in the field of angle measurement but also greatly expands the applicability of TENGs as self-powered sensors.
AB - Aiming at the problem of complex structure, dependence on complex materials and external power supply of vehicle pedal angle sensor. A quantization model of pedal movement based on the fan-shaped sector-single electrode sliding mode triboelectric nanogenerator (S-SETENG) is proposed. First, on the basis of the single electrode sliding mode triboelectric nanogenerator (SETENG), according to the potential law between the contact area and the open circuit voltage value, the S-SETENG that can obtain pedal movement information is developed. Secondly, through the simulated driving experiment, the pedal movement data in the natural driving state is obtained, including the pedal angle data output by the driving simulator and the voltage data output by S-SETENG. Then, using S-SETENG voltage data and vehicle pedal angle data to complete the training of pedal movement quantification model. Finally, according to the results of the prediction models on the test set, the acceleration pedal movement quantization model based on gate recurrent unit (GRU) and the brake pedal movement quantization model based on long-short term memory (LSTM) perform best, and the value of R square(R2) exceeds 0.94, which proves the accuracy and feasibility of this method. This not only presents a new principle in the field of angle measurement but also greatly expands the applicability of TENGs as self-powered sensors.
KW - quantitative model
KW - recurrent neural network
KW - triboelectric nanogenerators
KW - vehicle pedal movement
UR - http://www.scopus.com/inward/record.url?scp=85141671814&partnerID=8YFLogxK
U2 - 10.3901/JME.2022.17.215
DO - 10.3901/JME.2022.17.215
M3 - 文章
AN - SCOPUS:85141671814
SN - 0577-6686
VL - 58
SP - 215
EP - 225
JO - Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering
JF - Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering
IS - 17
ER -