Energy Management Strategy for Fuel Cell Vehicles Based on Online Driving Condition Recognition Using Dual-Model Predictive Control
Given the urgent challenges posed by global climate change and the ongoing energy crisis, fuel cell electric vehicles (FCEVs) have emerged as a promising solution. Incorporating sophisticated energy management strategies (EMSs) into FCEVs can significantly enhance the efficiency of the complex power...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7647 |
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| author | Fuxiang Li Xiaolin Wang Xucong Bao Ziyu Wang Ruixuan Li |
| author_facet | Fuxiang Li Xiaolin Wang Xucong Bao Ziyu Wang Ruixuan Li |
| author_sort | Fuxiang Li |
| collection | DOAJ |
| description | Given the urgent challenges posed by global climate change and the ongoing energy crisis, fuel cell electric vehicles (FCEVs) have emerged as a promising solution. Incorporating sophisticated energy management strategies (EMSs) into FCEVs can significantly enhance the efficiency of the complex powertrain under diverse driving conditions. In this paper, a dual-model predictive control energy management strategy based on long short-term memory (LSTM)-based driving condition recognition is proposed to enhance the economic performance of FCEVs and robustness across diverse driving conditions. Firstly, to improve the generalization capability and adaptability of the LSTM model and to enhance the accuracy of driving condition recognition, wavelet transform (WT) is introduced into both the offline training and online application of LSTM. Secondly, to enhance the real-time performance and control effectiveness of the EMS, model predictive control (MPC) and explicit model predictive control (eMPC) are established based on a unified optimization objective and constraints. Thirdly, a dual MPC switching logic is developed using the information of driving condition prediction, ensuring the coordination of dual MPCs in practical applications and enhancing their adaptability to various conditions. Finally, an evaluation of the simulations demonstrates that the proposed dual-model predictive control energy management strategy based on wavelet transform LSTM driving condition recognition (WTL-DMPC EMS) can improve economic performance. Compared with other baselines, the energy-saving capability is remarkable, showcasing its promising performance. |
| format | Article |
| id | doaj-art-ad9a8f899c79494dacd84d0b08f0e478 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-ad9a8f899c79494dacd84d0b08f0e4782025-08-20T01:55:45ZengMDPI AGSensors1424-82202024-11-012423764710.3390/s24237647Energy Management Strategy for Fuel Cell Vehicles Based on Online Driving Condition Recognition Using Dual-Model Predictive ControlFuxiang Li0Xiaolin Wang1Xucong Bao2Ziyu Wang3Ruixuan Li4College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Street, Jiangning District, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Street, Jiangning District, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Street, Jiangning District, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Street, Jiangning District, Nanjing 211106, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29, Jiangjun Street, Jiangning District, Nanjing 211106, ChinaGiven the urgent challenges posed by global climate change and the ongoing energy crisis, fuel cell electric vehicles (FCEVs) have emerged as a promising solution. Incorporating sophisticated energy management strategies (EMSs) into FCEVs can significantly enhance the efficiency of the complex powertrain under diverse driving conditions. In this paper, a dual-model predictive control energy management strategy based on long short-term memory (LSTM)-based driving condition recognition is proposed to enhance the economic performance of FCEVs and robustness across diverse driving conditions. Firstly, to improve the generalization capability and adaptability of the LSTM model and to enhance the accuracy of driving condition recognition, wavelet transform (WT) is introduced into both the offline training and online application of LSTM. Secondly, to enhance the real-time performance and control effectiveness of the EMS, model predictive control (MPC) and explicit model predictive control (eMPC) are established based on a unified optimization objective and constraints. Thirdly, a dual MPC switching logic is developed using the information of driving condition prediction, ensuring the coordination of dual MPCs in practical applications and enhancing their adaptability to various conditions. Finally, an evaluation of the simulations demonstrates that the proposed dual-model predictive control energy management strategy based on wavelet transform LSTM driving condition recognition (WTL-DMPC EMS) can improve economic performance. Compared with other baselines, the energy-saving capability is remarkable, showcasing its promising performance.https://www.mdpi.com/1424-8220/24/23/7647energy management strategydual-model predictive controldriving condition recognitionlong short-term memoryfuel cell vehicles |
| spellingShingle | Fuxiang Li Xiaolin Wang Xucong Bao Ziyu Wang Ruixuan Li Energy Management Strategy for Fuel Cell Vehicles Based on Online Driving Condition Recognition Using Dual-Model Predictive Control Sensors energy management strategy dual-model predictive control driving condition recognition long short-term memory fuel cell vehicles |
| title | Energy Management Strategy for Fuel Cell Vehicles Based on Online Driving Condition Recognition Using Dual-Model Predictive Control |
| title_full | Energy Management Strategy for Fuel Cell Vehicles Based on Online Driving Condition Recognition Using Dual-Model Predictive Control |
| title_fullStr | Energy Management Strategy for Fuel Cell Vehicles Based on Online Driving Condition Recognition Using Dual-Model Predictive Control |
| title_full_unstemmed | Energy Management Strategy for Fuel Cell Vehicles Based on Online Driving Condition Recognition Using Dual-Model Predictive Control |
| title_short | Energy Management Strategy for Fuel Cell Vehicles Based on Online Driving Condition Recognition Using Dual-Model Predictive Control |
| title_sort | energy management strategy for fuel cell vehicles based on online driving condition recognition using dual model predictive control |
| topic | energy management strategy dual-model predictive control driving condition recognition long short-term memory fuel cell vehicles |
| url | https://www.mdpi.com/1424-8220/24/23/7647 |
| work_keys_str_mv | AT fuxiangli energymanagementstrategyforfuelcellvehiclesbasedononlinedrivingconditionrecognitionusingdualmodelpredictivecontrol AT xiaolinwang energymanagementstrategyforfuelcellvehiclesbasedononlinedrivingconditionrecognitionusingdualmodelpredictivecontrol AT xucongbao energymanagementstrategyforfuelcellvehiclesbasedononlinedrivingconditionrecognitionusingdualmodelpredictivecontrol AT ziyuwang energymanagementstrategyforfuelcellvehiclesbasedononlinedrivingconditionrecognitionusingdualmodelpredictivecontrol AT ruixuanli energymanagementstrategyforfuelcellvehiclesbasedononlinedrivingconditionrecognitionusingdualmodelpredictivecontrol |