A stochastic power management strategy with skid avoidance for improving energy efficiency of in-wheel motor electric vehicles
Bibliographic record
Abstract
In this study, a stochastic power management strategy for in-wheel motor electric vehicles is proposed to reduce the energy consumption and increase the driving range, considering the unpredictable nature of the driving power demand. A stochastic dynamic programming approach, policy iteration algorithm, is used to create an infinite horizon problem formulation to calculate optimal power distribution policies for the vehicle. The developed stochastic dynamic programming strategy distributes the demanded power, P dem between the front and rear in-wheel motors by considering states of the vehicle, including the vehicle speed and the front and the rear wheels’ slip ratios. In addition, a skid avoidance rule is added to the power management strategy to maintain the wheels’ slip ratios within the desired values. Undesirable slip ratios cause poor brake and traction control performances and therefore should be avoided. The resulting strategy consists of a time-invariant, rule-based controller which is fast enough for real time implementations, and additionally, it is not expensive to be launched since the future power demand is approximated without a need to vehicle communication systems or telemetric capability. A high-fidelity model of an in-wheel motor electric vehicle is developed in the Autonomie/Simulink environment for evaluating the proposed strategy. The simulation results show that the proposed stochastic dynamic programming strategy is more efficient in comparison to some benchmark strategies, such as an equal power distribution and generalized rule-based dynamic programming. The simulation results of different driving scenarios for the considered in-wheel motor electric vehicle show the proposed power management strategy leads to 3% energy consumption reduction in average, at no additional cost. If the resulting energy savings is considered for the total annual trips for the vehicle and also the total number of electric vehicles in the country, the proposed power management strategy has a significant impact.
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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".