Energy Management in Refrigerated Electric Small Transport: A Hierarchical Approach
As the delivery industry undergoes a transition with the introduction of small electric transport vehicles, it is crucial to maximize their energy efficiency. Especially for vehicles with cooling chambers, innovative control strategies tailored for the simple onboard hardware are necessary. The pres...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10769090/ |
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| author | Agnes Poks Alexander Schirrer Martin Kozek |
| author_facet | Agnes Poks Alexander Schirrer Martin Kozek |
| author_sort | Agnes Poks |
| collection | DOAJ |
| description | As the delivery industry undergoes a transition with the introduction of small electric transport vehicles, it is crucial to maximize their energy efficiency. Especially for vehicles with cooling chambers, innovative control strategies tailored for the simple onboard hardware are necessary. The presented approach utilizes two-layer optimization, based on an offline optimization layer and a simple real-time controller. Offline optimization prior to the driving mission uses dynamic programming to ensure optimal operation. The approach involves performing backward and forward recursion, computing the cost-to-go matrix at each step by sweeping through the stage variable. The optimal control trajectory is obtained through forward dynamic programming in the second sweep. The optimized offline trajectory is stored onboard the vehicle, defining the control trajectory for a simple real-time control. This approach allows for a holistic optimal energy management using the limited computational resources in existing transport vehicles. Actual vehicle data and realistic driving missions are used for validations. The potential of the proposed control architecture is evaluated in simulations against mixed-integer predictive control and a rule-based proportional-integral controller. Results indicate energy savings ranging from 12.6 to 14.2% and enhancements in temperature regulation from 1.1 to 2.8%. |
| format | Article |
| id | doaj-art-c84d65ed0e214f73b07a6262e3a17ee5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c84d65ed0e214f73b07a6262e3a17ee52024-12-13T00:00:46ZengIEEEIEEE Access2169-35362024-01-011218391818393610.1109/ACCESS.2024.350767310769090Energy Management in Refrigerated Electric Small Transport: A Hierarchical ApproachAgnes Poks0https://orcid.org/0000-0002-0276-4552Alexander Schirrer1https://orcid.org/0000-0003-0331-0947Martin Kozek2Research Group of Control and Process Automation, TU Wien, Institute of Mechanics and Mechatronics, Vienna, AustriaResearch Group of Control and Process Automation, TU Wien, Institute of Mechanics and Mechatronics, Vienna, AustriaResearch Group of Control and Process Automation, TU Wien, Institute of Mechanics and Mechatronics, Vienna, AustriaAs the delivery industry undergoes a transition with the introduction of small electric transport vehicles, it is crucial to maximize their energy efficiency. Especially for vehicles with cooling chambers, innovative control strategies tailored for the simple onboard hardware are necessary. The presented approach utilizes two-layer optimization, based on an offline optimization layer and a simple real-time controller. Offline optimization prior to the driving mission uses dynamic programming to ensure optimal operation. The approach involves performing backward and forward recursion, computing the cost-to-go matrix at each step by sweeping through the stage variable. The optimal control trajectory is obtained through forward dynamic programming in the second sweep. The optimized offline trajectory is stored onboard the vehicle, defining the control trajectory for a simple real-time control. This approach allows for a holistic optimal energy management using the limited computational resources in existing transport vehicles. Actual vehicle data and realistic driving missions are used for validations. The potential of the proposed control architecture is evaluated in simulations against mixed-integer predictive control and a rule-based proportional-integral controller. Results indicate energy savings ranging from 12.6 to 14.2% and enhancements in temperature regulation from 1.1 to 2.8%.https://ieeexplore.ieee.org/document/10769090/Energy managementhierarchicaldynamic programmingmodel predictive controlelectrical vehiclesmixed-integer |
| spellingShingle | Agnes Poks Alexander Schirrer Martin Kozek Energy Management in Refrigerated Electric Small Transport: A Hierarchical Approach IEEE Access Energy management hierarchical dynamic programming model predictive control electrical vehicles mixed-integer |
| title | Energy Management in Refrigerated Electric Small Transport: A Hierarchical Approach |
| title_full | Energy Management in Refrigerated Electric Small Transport: A Hierarchical Approach |
| title_fullStr | Energy Management in Refrigerated Electric Small Transport: A Hierarchical Approach |
| title_full_unstemmed | Energy Management in Refrigerated Electric Small Transport: A Hierarchical Approach |
| title_short | Energy Management in Refrigerated Electric Small Transport: A Hierarchical Approach |
| title_sort | energy management in refrigerated electric small transport a hierarchical approach |
| topic | Energy management hierarchical dynamic programming model predictive control electrical vehicles mixed-integer |
| url | https://ieeexplore.ieee.org/document/10769090/ |
| work_keys_str_mv | AT agnespoks energymanagementinrefrigeratedelectricsmalltransportahierarchicalapproach AT alexanderschirrer energymanagementinrefrigeratedelectricsmalltransportahierarchicalapproach AT martinkozek energymanagementinrefrigeratedelectricsmalltransportahierarchicalapproach |