Numerical Modelling of the Electric Vehicle Cabin Cooling
The study of the cabin cooling system in an Electric Vehicle is vital to understand its energy consumption behaviour, where such information can be acted upon to better optimise and improve the overall energy consumption of the vehicle, thus translating into a longer driving range.Factors that cont...
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| Format: | Final Year Project / Dissertation / Thesis |
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2019
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| Online Access: | http://eprints.utar.edu.my/3472/ http://eprints.utar.edu.my/3472/1/ME%2D2019%2D1502719%2D1.pdf |
| _version_ | 1848885911221174272 |
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| author | Jong, Fabian Chin Peng |
| author_facet | Jong, Fabian Chin Peng |
| author_sort | Jong, Fabian Chin Peng |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | The study of the cabin cooling system in an Electric Vehicle is vital to understand its energy consumption behaviour, where such information can be acted upon to better optimise and improve the overall energy consumption of the vehicle, thus translating
into a longer driving range.Factors that contribute to the total thermal load in a cabin space are modelled,
where in Edinburgh has a value of 1880.14 W and 3136.14 W for Kuala Lumpur.Expansion is performed in the construction of the solar thermal load model, which
has the capability of calculating solar irradiance based on various inputs, which after validation produces a relative error of 7.17 %. The thermal load model is integrated into a refrigeration model at the cabin space subsystem in order to allow the study of
the effects of thermal loads on the performance of the refrigeration circuit using a single model. The model is validated for accuracy and it is found to have an average
relative error of 15.11 %. The thermal load model is also incorporated into a cabin temperaturepredicting
algorithm expansion. The refrigeration circuit model is also expanded to study the effects of battery heat generated from different driving cycles on the
performance of the refrigeration circuit, which requires maximum instantaneous power consumption of 140 W, 130 W and 138 W for UDDS, HWFET and US06 driving cycles. The model is also able to maintain the cabin temperature close to the targeted temperature, where the maximum deviation between the cabin temperatures to the targeted is only 1.09 %. Lastly, a study on the effects of the supporting,infrastructure is done, where it is concluded that the most optimal configuration is the one with triple glazing windows and extractor fans that is capable of reducing the combined thermal load and cabin temperature after 3600 seconds by 24.1 % and
20.5 % in Edinburgh and 42.68 % and 23.55 % in Kuala Lumpur. |
| first_indexed | 2025-11-15T19:30:07Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-3472 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:30:07Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-34722019-08-05T10:03:17Z Numerical Modelling of the Electric Vehicle Cabin Cooling Jong, Fabian Chin Peng TJ Mechanical engineering and machinery The study of the cabin cooling system in an Electric Vehicle is vital to understand its energy consumption behaviour, where such information can be acted upon to better optimise and improve the overall energy consumption of the vehicle, thus translating into a longer driving range.Factors that contribute to the total thermal load in a cabin space are modelled, where in Edinburgh has a value of 1880.14 W and 3136.14 W for Kuala Lumpur.Expansion is performed in the construction of the solar thermal load model, which has the capability of calculating solar irradiance based on various inputs, which after validation produces a relative error of 7.17 %. The thermal load model is integrated into a refrigeration model at the cabin space subsystem in order to allow the study of the effects of thermal loads on the performance of the refrigeration circuit using a single model. The model is validated for accuracy and it is found to have an average relative error of 15.11 %. The thermal load model is also incorporated into a cabin temperaturepredicting algorithm expansion. The refrigeration circuit model is also expanded to study the effects of battery heat generated from different driving cycles on the performance of the refrigeration circuit, which requires maximum instantaneous power consumption of 140 W, 130 W and 138 W for UDDS, HWFET and US06 driving cycles. The model is also able to maintain the cabin temperature close to the targeted temperature, where the maximum deviation between the cabin temperatures to the targeted is only 1.09 %. Lastly, a study on the effects of the supporting,infrastructure is done, where it is concluded that the most optimal configuration is the one with triple glazing windows and extractor fans that is capable of reducing the combined thermal load and cabin temperature after 3600 seconds by 24.1 % and 20.5 % in Edinburgh and 42.68 % and 23.55 % in Kuala Lumpur. 2019-04 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/3472/1/ME%2D2019%2D1502719%2D1.pdf Jong, Fabian Chin Peng (2019) Numerical Modelling of the Electric Vehicle Cabin Cooling. Final Year Project, UTAR. http://eprints.utar.edu.my/3472/ |
| spellingShingle | TJ Mechanical engineering and machinery Jong, Fabian Chin Peng Numerical Modelling of the Electric Vehicle Cabin Cooling |
| title | Numerical Modelling of the Electric Vehicle Cabin Cooling |
| title_full | Numerical Modelling of the Electric Vehicle Cabin Cooling |
| title_fullStr | Numerical Modelling of the Electric Vehicle Cabin Cooling |
| title_full_unstemmed | Numerical Modelling of the Electric Vehicle Cabin Cooling |
| title_short | Numerical Modelling of the Electric Vehicle Cabin Cooling |
| title_sort | numerical modelling of the electric vehicle cabin cooling |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.utar.edu.my/3472/ http://eprints.utar.edu.my/3472/1/ME%2D2019%2D1502719%2D1.pdf |