TY - GEN
T1 - The Optimal Chassis Configuration Matching for Distributed Hybrid Truck Working Conditions Based on Energy Consumption
AU - Liu, Jiwei
AU - Li, Junqiu
AU - Wei, Ruichuan
AU - Qiu, Meng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The distributed hybrid truck which carries the hub motor driving system has advantages of high efficiency, high controllability and high integration. It also shows potential to reducing energy consumption and range anxiety, becoming one of the future development trends of commercial vehicles. However, due to the large number of chassis components and the complex power units, the problem of economy has too many dimensions to find the optimal solution. In this paper, reasonable chassis configurations are selected and a distributed hybrid truck model is established. Then this paper classifies the working conditions into three types by using the historical test data, while divides the sample working conditions and extracts the features. Giving priority to energy consumption, the global optimal chassis configuration for each working condition is obtained based on the dynamic programming (DP) algorithm. Then the sample working conditions and the matching chassis configurations are used to train the long short-term memory (LSTM) neural network to study the relationship between the working condition and the corresponding optimal chassis configuration. Finally, concludes the whole paper and points out the direction of future work.
AB - The distributed hybrid truck which carries the hub motor driving system has advantages of high efficiency, high controllability and high integration. It also shows potential to reducing energy consumption and range anxiety, becoming one of the future development trends of commercial vehicles. However, due to the large number of chassis components and the complex power units, the problem of economy has too many dimensions to find the optimal solution. In this paper, reasonable chassis configurations are selected and a distributed hybrid truck model is established. Then this paper classifies the working conditions into three types by using the historical test data, while divides the sample working conditions and extracts the features. Giving priority to energy consumption, the global optimal chassis configuration for each working condition is obtained based on the dynamic programming (DP) algorithm. Then the sample working conditions and the matching chassis configurations are used to train the long short-term memory (LSTM) neural network to study the relationship between the working condition and the corresponding optimal chassis configuration. Finally, concludes the whole paper and points out the direction of future work.
KW - Chassis Configuration
KW - DP
KW - Distributed Hybrid Truck
KW - LSTM
KW - Working Condition
UR - http://www.scopus.com/inward/record.url?scp=85125176987&partnerID=8YFLogxK
U2 - 10.1109/CCDC52312.2021.9602516
DO - 10.1109/CCDC52312.2021.9602516
M3 - Conference contribution
AN - SCOPUS:85125176987
T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
SP - 540
EP - 545
BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Chinese Control and Decision Conference, CCDC 2021
Y2 - 22 May 2021 through 24 May 2021
ER -
