Material selection in Electric Vehicle Engineering Programs
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".