Simultaneous Plant/Controller Optimization of Traction Control for Electric Vehicle
Bibliographic record
Abstract
Development of electric vehicles is motivated by global concerns over the need \nfor environmental protection. In addition to its zero-emission characteristics, an \nelectric propulsion system enables high performance torque control that may be \nused to maximize vehicle performance obtained from energy-efficient, low rolling \nresistance tires typically associated with degraded road-holding ability. \nA simultaneous plant/controller optimization is performed on an electric vehicle \ntraction control system with respect to conflicting energy use and performance \nobjectives. Due to system nonlinearities, an iterative simulation-based optimization \napproach is proposed using a system model and a genetic algorithm (GA) to guide \nsearch space exploration. \nThe system model consists of: a drive cycle with a constant driver torque request \nand a step change in coefficient of friction, a single-wheel longitudinal vehicle model, \na tire model described using the Magic Formula and a constant rolling resistance, \nand an adhesion gradient fuzzy logic traction controller. \nOptimization is defined in terms of the all at once variable selection of: either \na performance oriented or low rolling resistance tire, the shape of a fuzzy logic \ncontroller membership function, and a set of fuzzy logic controller rule base conclusions. \nA mixed encoding, multi-chromosomal GA is implemented to represent the \nvariables, respectively, as a binary string, a real-valued number, and a novel rule \nbase encoding based on the definition of a partially ordered set (poset) by delta \ninclusion. \nSimultaneous optimization results indicate that, under straight-line acceleration \nand unless energy concerns are completely neglected, low rolling resistance tires \nshould be incorporated in a traction control system design since the energy saving \nbenefits outweigh the associated degradation in road-holding ability. The results \nalso indicate that the proposed novel encoding enables the efficient representation \nof a fix-sized fuzzy logic rule base within a GA.
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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 | 0.000 |
| 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".