Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning
Aiming to improve the distance per charge of four in-wheel independent motor-drive electric vehicles in intelligent transportation systems, a hierarchical energy management strategy that weighs their computational efficiency and optimization performance is proposed. According to the information of a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/6/1350 |
| _version_ | 1797612163611754496 |
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| author | Zhe Zhang Haitao Ding Konghui Guo Niaona Zhang |
| author_facet | Zhe Zhang Haitao Ding Konghui Guo Niaona Zhang |
| author_sort | Zhe Zhang |
| collection | DOAJ |
| description | Aiming to improve the distance per charge of four in-wheel independent motor-drive electric vehicles in intelligent transportation systems, a hierarchical energy management strategy that weighs their computational efficiency and optimization performance is proposed. According to the information of an intelligent transportation system, a method combining reinforcement learning with receding horizon optimization is proposed at the upper level, which solves the cruising velocity for eco-driving in a long predictive horizon based on the online construction of a velocity planning problem. At the lower level, a multi-objective optimal torque allocation method that considers energy saving and safety is proposed, where an analytical solution based on the state feedback control was obtained with the vehicle following the optimal speed of the upper level and tracking the centerline of the target path. The energy management strategy proposed in this study effectively reduces the complexity of the intelligent energy-saving control system of the vehicle and achieves a fast solution to the whole vehicle energy optimization problem, integrating macro-traffic information while considering both power and safety. Finally, an intelligent, connected hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this study. The simulation results demonstrate that the proposed method reduces energy consumption by 12.98% compared with the conventional constant-speed cruising strategy. In addition, the computational time is significantly reduced. |
| first_indexed | 2024-03-11T06:38:24Z |
| format | Article |
| id | doaj.art-01106be76bb24fdf8431011e73bd66fd |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T06:38:24Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-01106be76bb24fdf8431011e73bd66fd2023-11-17T10:44:18ZengMDPI AGElectronics2079-92922023-03-01126135010.3390/electronics12061350Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement LearningZhe Zhang0Haitao Ding1Konghui Guo2Niaona Zhang3State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, ChinaAiming to improve the distance per charge of four in-wheel independent motor-drive electric vehicles in intelligent transportation systems, a hierarchical energy management strategy that weighs their computational efficiency and optimization performance is proposed. According to the information of an intelligent transportation system, a method combining reinforcement learning with receding horizon optimization is proposed at the upper level, which solves the cruising velocity for eco-driving in a long predictive horizon based on the online construction of a velocity planning problem. At the lower level, a multi-objective optimal torque allocation method that considers energy saving and safety is proposed, where an analytical solution based on the state feedback control was obtained with the vehicle following the optimal speed of the upper level and tracking the centerline of the target path. The energy management strategy proposed in this study effectively reduces the complexity of the intelligent energy-saving control system of the vehicle and achieves a fast solution to the whole vehicle energy optimization problem, integrating macro-traffic information while considering both power and safety. Finally, an intelligent, connected hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this study. The simulation results demonstrate that the proposed method reduces energy consumption by 12.98% compared with the conventional constant-speed cruising strategy. In addition, the computational time is significantly reduced.https://www.mdpi.com/2079-9292/12/6/1350eco-drivingenergy managementreinforcement learningreceding optimizationtorque distribution |
| spellingShingle | Zhe Zhang Haitao Ding Konghui Guo Niaona Zhang Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning Electronics eco-driving energy management reinforcement learning receding optimization torque distribution |
| title | Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning |
| title_full | Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning |
| title_fullStr | Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning |
| title_full_unstemmed | Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning |
| title_short | Eco-Driving Cruise Control for 4WIMD-EVs Based on Receding Horizon Reinforcement Learning |
| title_sort | eco driving cruise control for 4wimd evs based on receding horizon reinforcement learning |
| topic | eco-driving energy management reinforcement learning receding optimization torque distribution |
| url | https://www.mdpi.com/2079-9292/12/6/1350 |
| work_keys_str_mv | AT zhezhang ecodrivingcruisecontrolfor4wimdevsbasedonrecedinghorizonreinforcementlearning AT haitaoding ecodrivingcruisecontrolfor4wimdevsbasedonrecedinghorizonreinforcementlearning AT konghuiguo ecodrivingcruisecontrolfor4wimdevsbasedonrecedinghorizonreinforcementlearning AT niaonazhang ecodrivingcruisecontrolfor4wimdevsbasedonrecedinghorizonreinforcementlearning |