Extending Battery Lifetime of Electric Mining Vehicles through Thermal and Duty Cycle Management
Bibliographic record
Abstract
We have performed a study to identify how aspects such as driving cycle, thermal management, and road conditions impact the lifetime of a battery pack in electric mining vehicles. Many factors including safety, lifetime and the available power of li-ion batteries are influenced by temperature. With a direct impact on cell aging and degradation; battery thermal management can therefore be considered a key component of electric vehicles. Collaborating on a project with Nouveau Monde Graphite (NMG), the company set to build the world’s first fully-electric open pit mine, we analyzed the impact of both the battery thermal management and the duty cycle on the battery lifetime of a typical electric hauling vehicle. We first built a system level model of the electric truck based on a known diesel hauling truck (Western Star 6900) in the software ADVISOR. We then defined various hauling paths in the software, observing and calculating the impact of ambient temperature, initial battery temperature and depth-of-discharge variations at the end of each cycle. A cell aging model for the three available commercial NMC cells was then used to evaluate the cell lifetime based on 70% cell end-of-life criteria. Presented in this study are the effect of temperature on battery aging as well as our recommendations for duty cycle modifications to maximize the battery lifetime.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".