A two-layered eco-cooling control strategy for electric car air conditioning systems with integration of dynamic programming and fuzzy PID
•A two layered control strategy integrating DP and fuzzy PID is built.•PPTC can accurately describe the thermal habit of passenger.•DP integrates the PPTC, VVP and WIR and achieves a low energy cost.•Fuzzy PID well responses the requirement of refrigeration capacity from DP.•Two layered strategy rai...
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| Vydáno v: | Applied thermal engineering Ročník 211; s. 118488 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Oxford
Elsevier Ltd
05.07.2022
Elsevier BV |
| Témata: | |
| ISSN: | 1359-4311, 1873-5606 |
| On-line přístup: | Získat plný text |
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| Abstract | •A two layered control strategy integrating DP and fuzzy PID is built.•PPTC can accurately describe the thermal habit of passenger.•DP integrates the PPTC, VVP and WIR and achieves a low energy cost.•Fuzzy PID well responses the requirement of refrigeration capacity from DP.•Two layered strategy raises comfort and control precision and saves energy.
A two-layered control strategy is proposed for the air conditioning (AC) systems of electric vehicles. Unlike traditional rule-based controllers such as the on–off controller and proportion-integral-derivative (PID) controller, this strategy includes a decision layer and a control strategy. The core algorithm in the decision layer is the dynamic programming (DP), which integrates information from the thermal habit predictor of the passenger, vehicle velocity planner, and weather information receiver. The DP optimises the development of the cabin temperature to minimise the energy consumption of the AC system and sends the planned temperature to the control layer. The control layer uses a fuzzy PID algorithm to adjust the compressor speed based on the planned temperature profile, such that the real-world cabin temperature approaches the planned temperature. This two-layered control strategy is applied to a car whose AC-cabin system was verified by test data, and the results are compared with those obtained by the on–off controller and PID. When the target cabin temperature is not manually adjusted, the energy cost of the proposed strategy is 28.2% and 5.4% lower than those of the on–off controller and PID, respectively, at the ambient temperature profile of Environment 1 (described herein), and its maximum fluctuation of the cabin temperature is 92.8% and 68.2% smaller than those of the on–off controller and PID, respectively. At the ambient temperature of Environment 2 (described herein, lower than that of Environment 1), the energy cost of the proposed strategy is 37.1% and 5.9% lower, and the maximum fluctuation of the cabin temperature is 96.8% and 86.4% smaller, compared to the on–off controller and PID, respectively. When the target temperature is repeatedly set for the on–off controller and PID (first to 20 °C, then to 24.3 °C), the AC system consumes extra energy, leading to poor thermal comfort. Because the proposed strategy automatically sets the cabin temperature to the temperature preferred by the passenger, there is no extra adjustment of the target and the thermal environment inside the cabin is optimal for the passenger. Under this condition, the developed strategy can produce energy savings of 30.2% and 12.4%, compared to the on–off strategy and PID, respectively. Thus, the two-layered strategy can control the cabin temperature precisely, provide the passenger with a good thermal environment, and produce energy savings for the AC system. |
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| AbstractList | •A two layered control strategy integrating DP and fuzzy PID is built.•PPTC can accurately describe the thermal habit of passenger.•DP integrates the PPTC, VVP and WIR and achieves a low energy cost.•Fuzzy PID well responses the requirement of refrigeration capacity from DP.•Two layered strategy raises comfort and control precision and saves energy.
A two-layered control strategy is proposed for the air conditioning (AC) systems of electric vehicles. Unlike traditional rule-based controllers such as the on–off controller and proportion-integral-derivative (PID) controller, this strategy includes a decision layer and a control strategy. The core algorithm in the decision layer is the dynamic programming (DP), which integrates information from the thermal habit predictor of the passenger, vehicle velocity planner, and weather information receiver. The DP optimises the development of the cabin temperature to minimise the energy consumption of the AC system and sends the planned temperature to the control layer. The control layer uses a fuzzy PID algorithm to adjust the compressor speed based on the planned temperature profile, such that the real-world cabin temperature approaches the planned temperature. This two-layered control strategy is applied to a car whose AC-cabin system was verified by test data, and the results are compared with those obtained by the on–off controller and PID. When the target cabin temperature is not manually adjusted, the energy cost of the proposed strategy is 28.2% and 5.4% lower than those of the on–off controller and PID, respectively, at the ambient temperature profile of Environment 1 (described herein), and its maximum fluctuation of the cabin temperature is 92.8% and 68.2% smaller than those of the on–off controller and PID, respectively. At the ambient temperature of Environment 2 (described herein, lower than that of Environment 1), the energy cost of the proposed strategy is 37.1% and 5.9% lower, and the maximum fluctuation of the cabin temperature is 96.8% and 86.4% smaller, compared to the on–off controller and PID, respectively. When the target temperature is repeatedly set for the on–off controller and PID (first to 20 °C, then to 24.3 °C), the AC system consumes extra energy, leading to poor thermal comfort. Because the proposed strategy automatically sets the cabin temperature to the temperature preferred by the passenger, there is no extra adjustment of the target and the thermal environment inside the cabin is optimal for the passenger. Under this condition, the developed strategy can produce energy savings of 30.2% and 12.4%, compared to the on–off strategy and PID, respectively. Thus, the two-layered strategy can control the cabin temperature precisely, provide the passenger with a good thermal environment, and produce energy savings for the AC system. A two-layered control strategy is proposed for the air conditioning (AC) systems of electric vehicles. Unlike traditional rule-based controllers such as the on–off controller and proportion-integral-derivative (PID) controller, this strategy includes a decision layer and a control strategy. The core algorithm in the decision layer is the dynamic programming (DP), which integrates information from the thermal habit predictor of the passenger, vehicle velocity planner, and weather information receiver. The DP optimises the development of the cabin temperature to minimise the energy consumption of the AC system and sends the planned temperature to the control layer. The control layer uses a fuzzy PID algorithm to adjust the compressor speed based on the planned temperature profile, such that the real-world cabin temperature approaches the planned temperature. This two-layered control strategy is applied to a car whose AC-cabin system was verified by test data, and the results are compared with those obtained by the on–off controller and PID. When the target cabin temperature is not manually adjusted, the energy cost of the proposed strategy is 28.2% and 5.4% lower than those of the on–off controller and PID, respectively, at the ambient temperature profile of Environment 1 (described herein), and its maximum fluctuation of the cabin temperature is 92.8% and 68.2% smaller than those of the on–off controller and PID, respectively. At the ambient temperature of Environment 2 (described herein, lower than that of Environment 1), the energy cost of the proposed strategy is 37.1% and 5.9% lower, and the maximum fluctuation of the cabin temperature is 96.8% and 86.4% smaller, compared to the on–off controller and PID, respectively. When the target temperature is repeatedly set for the on–off controller and PID (first to 20 °C, then to 24.3 °C), the AC system consumes extra energy, leading to poor thermal comfort. Because the proposed strategy automatically sets the cabin temperature to the temperature preferred by the passenger, there is no extra adjustment of the target and the thermal environment inside the cabin is optimal for the passenger. Under this condition, the developed strategy can produce energy savings of 30.2% and 12.4%, compared to the on–off strategy and PID, respectively. Thus, the two-layered strategy can control the cabin temperature precisely, provide the passenger with a good thermal environment, and produce energy savings for the AC system. |
| ArticleNumber | 118488 |
| Author | Li, Kuining Zhou, Yi Qian, Yuping Yang, Peng Xie, Yi Zhang, Yangjun |
| Author_xml | – sequence: 1 givenname: Yi orcidid: 0000-0002-1811-2997 surname: Xie fullname: Xie, Yi email: claudexie@cqu.edu.cn organization: College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China – sequence: 2 givenname: Peng surname: Yang fullname: Yang, Peng organization: College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China – sequence: 3 givenname: Yuping surname: Qian fullname: Qian, Yuping email: qianyuping@tsinghua.edu.cn organization: State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China – sequence: 4 givenname: Yangjun surname: Zhang fullname: Zhang, Yangjun organization: State Key Lab of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China – sequence: 5 givenname: Kuining surname: Li fullname: Li, Kuining organization: School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China – sequence: 6 givenname: Yi surname: Zhou fullname: Zhou, Yi organization: Chongqing Chang’an New Energy Vehicle Technology Co. Ltd, Chongqing 401120, China |
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| Keywords | Dynamic programming algorithm Velocity planning Air conditioning system Cabin temperature Passenger’s thermal comfort Energy saving |
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| Snippet | •A two layered control strategy integrating DP and fuzzy PID is built.•PPTC can accurately describe the thermal habit of passenger.•DP integrates the PPTC, VVP... A two-layered control strategy is proposed for the air conditioning (AC) systems of electric vehicles. Unlike traditional rule-based controllers such as the... |
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| SubjectTerms | Air conditioning Air conditioning system Algorithms Ambient temperature Cabin temperature Closed loop systems Controllers Dynamic programming Dynamic programming algorithm Electric vehicles Energy consumption Energy costs Energy saving Fuzzy control Heat transfer Meteorological data Passengers Passenger’s thermal comfort Proportional integral derivative Temperature Temperature profiles Thermal comfort Thermal environments Velocity planning |
| Title | A two-layered eco-cooling control strategy for electric car air conditioning systems with integration of dynamic programming and fuzzy PID |
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