A stochastic power management strategy with skid avoidance for improving\n energy efficiency of in-wheel motor electric vehicles
Bibliographic record
Abstract
In this study, a stochastic power management strategy for in-wheel motor\nelectric vehicles (IWM-EV) is proposed to reduce the energy consumption and\nincrease the driving range by considering the unpredictable nature of the\ndriving power demand. A stochastic dynamic programming (SDP) approach, policy\niteration algorithm, is used to create an infinite horizon problem formulation\nto calculate optimal power distribution policies for the vehicle. The developed\nSDP strategy distributes the demanded power, between the front and rear IWMs by\nconsidering states of the vehicle, including the vehicle speed and the front\nand the rear wheels' slip ratios. In addition, a skid avoidance rule is added\nto the power management strategy to maintain the wheels' slip ratios within the\ndesired values. Undesirable slip ratios cause poor brake and traction control\nperformances and therefore should be avoided. The resulting strategy consists\nof a time- invariant, rule-based controller which is fast enough for real-time\nimplementations, and additionally, it is not expensive to be launched since the\nfuture power demand is approximated without a need to vehicle communication\nsystems or telemetric capability. A high-fidelity model of an IWM-EV is\ndeveloped in the Autonomie/Simulink environment for evaluating the proposed\nstrategy. The simulation results show that the proposed SDP strategy is more\nefficient in comparison to some benchmark strategies, such as an equal power\ndistribution (ED) and generalized rule- based dynamic programming (GRDP). The\nsimulation results of different driving scenarios for the considered IWM-EV\nshows the proposed power management strategy leads to considerable energy\nconsumption reduction in average, at no additional cost.\n
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".