Improved regenerative braking in electric vehicles through switch selection optimization
Bibliographic record
Abstract
Transportation electrification is at the core of the possible solutions to many challenges the world is currently facing. Efficient vehicle electrification has the potential to simultaneously reduce greenhouse gasses emissions and to tackle range anxiety issues. Among different strategies for advancements in Electric Vehicle (EV) efficiency, enhancing Regenerative Braking (REGEN) capabilities is an area with opportunities. As REGEN faces different impediments, upgrades in safety, efficiency, and/or battery quality of life are usually accompanied with further strain in energy management schemes, limiting REGEN performance. Power Electronics (PE) improvements are among the options that have the potential to benefit REGEN and overall efficiency. This work proposes a method to improves REGEN without adding extra stress on the other aspects that limit its performance, by optimizing PE-stage switch selection using openly available, manufacturer-provided data. To do so, the thesis develops a flexible simulation platform capable of: 1) integrating various subsystem modeling approaches, 2) analyzing different EV configurations, architectures, and components, and 3) analyzing the dynamic behavior of the Battery Electric Vehicle (BEV) while maintaining low simulation time. It also adopts a multiobjective optimization approach that gives the user freedom to define the weight of the objectives, as well as to include new objectives at any time - as long as the initial design choices do not change. The combination of a simulation platform suited for model-based design and an optimization formulation yields a method that fits well within the Design Automation (DA) framework. Therefore, the thesis is constructed with the framework as a guideline. The simulations show that proper switch selection can improve REGEN by over 18% and EV range efficiency by over 20%. The solution is corroborated by the results of the sensitivity and the robustness analysis.
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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 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".