Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles
This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investig...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/11/12/356 |
| _version_ | 1797461986979610624 |
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| author | Fei Ju Nikolce Murgovski Weichao Zhuang Liangmo Wang |
| author_facet | Fei Ju Nikolce Murgovski Weichao Zhuang Liangmo Wang |
| author_sort | Fei Ju |
| collection | DOAJ |
| description | This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time. |
| first_indexed | 2024-03-09T17:28:10Z |
| format | Article |
| id | doaj.art-9b341920299148e3ae5b2acf17851b20 |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-09T17:28:10Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-9b341920299148e3ae5b2acf17851b202023-11-24T12:35:07ZengMDPI AGActuators2076-08252022-12-01111235610.3390/act11120356Integrated Propulsion and Cabin-Cooling Management for Electric VehiclesFei Ju0Nikolce Murgovski1Weichao Zhuang2Liangmo Wang3School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaElectrical Engineering, Chalmers University of Technology, 41296 Gothenburg, SwedenSchool of Mechanical Engineering, Southeast University, Nanjing 211189, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaThis paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time.https://www.mdpi.com/2076-0825/11/12/356eco-drivingspeed planningcabin thermal managementmodel predictive controlelectric vehicle |
| spellingShingle | Fei Ju Nikolce Murgovski Weichao Zhuang Liangmo Wang Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles Actuators eco-driving speed planning cabin thermal management model predictive control electric vehicle |
| title | Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles |
| title_full | Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles |
| title_fullStr | Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles |
| title_full_unstemmed | Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles |
| title_short | Integrated Propulsion and Cabin-Cooling Management for Electric Vehicles |
| title_sort | integrated propulsion and cabin cooling management for electric vehicles |
| topic | eco-driving speed planning cabin thermal management model predictive control electric vehicle |
| url | https://www.mdpi.com/2076-0825/11/12/356 |
| work_keys_str_mv | AT feiju integratedpropulsionandcabincoolingmanagementforelectricvehicles AT nikolcemurgovski integratedpropulsionandcabincoolingmanagementforelectricvehicles AT weichaozhuang integratedpropulsionandcabincoolingmanagementforelectricvehicles AT liangmowang integratedpropulsionandcabincoolingmanagementforelectricvehicles |