Predictive battery thermal management using quantile convolutional neural networks
An improvement in energy efficiency of Battery Thermal Management Systems (BTMS) can increase range and reduce well-to-wheel emissions of Battery Electric Vehicles (BEV). In this work, the potential of a predictive BTMS using Quantile Convolutional Neural Networks (QCNN) was examined. The QCNN provi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Transportation Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666691X22000483 |
| _version_ | 1811178820825251840 |
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| author | Andreas M. Billert Stefan Erschen Michael Frey Frank Gauterin |
| author_facet | Andreas M. Billert Stefan Erschen Michael Frey Frank Gauterin |
| author_sort | Andreas M. Billert |
| collection | DOAJ |
| description | An improvement in energy efficiency of Battery Thermal Management Systems (BTMS) can increase range and reduce well-to-wheel emissions of Battery Electric Vehicles (BEV). In this work, the potential of a predictive BTMS using Quantile Convolutional Neural Networks (QCNN) was examined. The QCNN provided quantile predictions of battery temperature based on input data from both previous and following drive segments. The predictive control was designed to choose battery cooling thresholds based on a weighted sum of battery cooling, ageing and derating costs derived by the quantile predictions. The predictive BTMS was analyzed concerning its adaptability to different routes ahead, tunability of cost weights as well as robustness to uncertainty of inputs. A setup with unchanged ageing costs reduced average cooling costs by 9% compared to a fixed threshold strategy in a set of 18 scenarios. Simplifications and limitations were discussed to provide a base for further improvements, for example concerning the limited freedom of cooling threshold choice. In conclusion, the developed framework was able to use QCNN predictions to increase the BTMS energy efficiency while taking ageing and derating effects into account. |
| first_indexed | 2024-04-11T06:25:23Z |
| format | Article |
| id | doaj.art-a3df35aabac3412abe2ed48285f5131d |
| institution | Directory Open Access Journal |
| issn | 2666-691X |
| language | English |
| last_indexed | 2024-04-11T06:25:23Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Engineering |
| spelling | doaj.art-a3df35aabac3412abe2ed48285f5131d2022-12-22T04:40:24ZengElsevierTransportation Engineering2666-691X2022-12-0110100150Predictive battery thermal management using quantile convolutional neural networksAndreas M. Billert0Stefan Erschen1Michael Frey2Frank Gauterin3Corresponding author.; Karlsruhe Institute of Technology (KIT), Institute of Vehicle System Technology, 76131 Karlsruhe, Germany; Bayerische Motoren Werke AG, 80788 Munich, GermanyBayerische Motoren Werke AG, 80788 Munich, GermanyKarlsruhe Institute of Technology (KIT), Institute of Vehicle System Technology, 76131 Karlsruhe, GermanyKarlsruhe Institute of Technology (KIT), Institute of Vehicle System Technology, 76131 Karlsruhe, GermanyAn improvement in energy efficiency of Battery Thermal Management Systems (BTMS) can increase range and reduce well-to-wheel emissions of Battery Electric Vehicles (BEV). In this work, the potential of a predictive BTMS using Quantile Convolutional Neural Networks (QCNN) was examined. The QCNN provided quantile predictions of battery temperature based on input data from both previous and following drive segments. The predictive control was designed to choose battery cooling thresholds based on a weighted sum of battery cooling, ageing and derating costs derived by the quantile predictions. The predictive BTMS was analyzed concerning its adaptability to different routes ahead, tunability of cost weights as well as robustness to uncertainty of inputs. A setup with unchanged ageing costs reduced average cooling costs by 9% compared to a fixed threshold strategy in a set of 18 scenarios. Simplifications and limitations were discussed to provide a base for further improvements, for example concerning the limited freedom of cooling threshold choice. In conclusion, the developed framework was able to use QCNN predictions to increase the BTMS energy efficiency while taking ageing and derating effects into account.http://www.sciencedirect.com/science/article/pii/S2666691X22000483Battery thermal managementMachine learningPredictive controlQuantile convolutional neural networks |
| spellingShingle | Andreas M. Billert Stefan Erschen Michael Frey Frank Gauterin Predictive battery thermal management using quantile convolutional neural networks Transportation Engineering Battery thermal management Machine learning Predictive control Quantile convolutional neural networks |
| title | Predictive battery thermal management using quantile convolutional neural networks |
| title_full | Predictive battery thermal management using quantile convolutional neural networks |
| title_fullStr | Predictive battery thermal management using quantile convolutional neural networks |
| title_full_unstemmed | Predictive battery thermal management using quantile convolutional neural networks |
| title_short | Predictive battery thermal management using quantile convolutional neural networks |
| title_sort | predictive battery thermal management using quantile convolutional neural networks |
| topic | Battery thermal management Machine learning Predictive control Quantile convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2666691X22000483 |
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