Material selection in Electric Vehicle Engineering Programs
Notice bibliographique
Résumé
Abstract No one could have missed the transition towards electrification in society, with the surge in electric cars and other vehicles on the streets around us. This is partly driven by the realization that fossil fuels need to be phased out and partly by other environmental concerns. It is also boosted by technological developments of battery performance, enabling more energy to be stored electrochemically using new and better materials. Furthermore, there are new appealing modes of transport, such as electric skateboards, hoverboards and monowheels. Such topics are popular with students of mechanical and electrical engineering, as well as in product development and design projects. In this paper, we describe how sustainability and design have been systematically introduced, using a materials approach, into an undergraduate program of electrical engineering (EE) with electric vehicle specialization as well as in a one-year graduate program on electrical vehicle engineering. This was done using three materials-focused computer labs, dealing progressively with (i) material properties and selection, (ii) eco design and lifecycle thinking and (iii) battery design, each embedded within a different EE class. A well-known materials education software, Granta EduPack, covering all these areas was used as the learning platform. The purpose of the study was to gauge the interest and perceived usefulness of materials knowledge by around 40 EE students using this approach. It was conducted by integrating 5 survey questions into the end of student assignments before and after the second lab instalment mentioned above (eco design and lifecycle thinking). Both groups think MS&E is quite interesting (3.6-4.0 out of 5). They also think materials and material knowledge are important to their education (4.1-4.4 out of 5). As additional information that could be extracted from the surveys, we learned that the computer lab itself resulted in a significant increase in the self-assessed knowledge and skills linked to the content. We conclude that elements from materials science and engineering can be a successful and well-appreciated approach to introducing sustainability and design into non-mechanical engineering programs, such as electrical vehicle engineering. With this paper, we are hoping to share details and experiences of this materials-led approach and get feed-back from the wider materials community.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».