Real-Time Optimization of Energy Management Strategy for Fuel Cell Vehicles Using Inflated 3D Inception Long Short-Term Memory Network-Based Speed Prediction

The performance of speed prediction-based energy management strategy (EMS) for fuel cell vehicles (FCVs) highly relies on the accuracy of predicted speed sequences. Therefore, the future speed sequences are estimated by Inflated 3D Inception long short-term memory (LSTM) network, which can use the h...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 70; H. 2; S. 1190 - 1199
Hauptverfasser: Zhang, Caizhi, Zhang, Yuanzhi, Huang, Zhiyu, Lv, Chen, Hao, Dong, Liang, Chen, Deng, Chenghao, Chen, Jinrui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Degradation
Durability
Electric vehicles
Energy consumption
Energy management
energy management strategy
Fuel cell vehicles
Fuel cells
Inflated 3D Inception LSTM network
Lithium-ion batteries
Mechanical power transmission
Optimization
Powertrain
Quadratic programming
Real time
Real-time optimization
Real-time systems
Sequences
sequential quadratic programming algorithm
State of charge
ISSN:0018-9545, 1939-9359
Online-Zugang:Volltext
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Zusammenfassung:The performance of speed prediction-based energy management strategy (EMS) for fuel cell vehicles (FCVs) highly relies on the accuracy of predicted speed sequences. Therefore, the future speed sequences are estimated by Inflated 3D Inception long short-term memory (LSTM) network, which can use the historical speed and image information to improve the accuracy of speed prediction. Meanwhile, the energy economy and powertrain system durability are the objectives of real-time optimization. For optimizing energy economy and powertrain system durability of FCVs, the real-time optimization of EMS using the Inflated 3D Inception LSTM network-based speed prediction is proposed. To do this, the mathematical models including energy economy and powertrain system durability of FCVs are developed at the beginning. Then, based on the predicted speed sequences, a real-time optimization method with sequential quadratic programming (SQP) algorithm is proposed to minimize the energy consumption and take into consideration powertrain system degradation in the prediction horizon. Simulation results show that the proposed EMS can significantly reduce the total cost of energy consumption and powertrain system degradation.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3051201