Implementation of Wavelet Controller for Battery Storage System of Hybrid Electric Vehicle
Bibliographic record
Abstract
This paper presents the development and implementation of a wavelet based multiresolution proportional integral derivate (MRPID) controller for the temperature control of ambient air of battery storage system of hybrid electric vehicle. In the proposed MRPID controller, the discrete wavelet transform (DWT) is used to decompose the error between actual and set temperatures of ambient air of the battery storage system into different frequency components at various scales. The wavelet transformed coefficients of the temperature error at different scales of the DWT are scaled by the suitable gains and are added together to generate the control signal of the MRPID controller for the battery storage system. The performances of the proposed wavelet controller are investigated in simulation and experiments for different operating conditions of the battery storage system. The proposed wavelet controller is implemented in real time for the process thermal system PT326 using the digital signal processor board ds1102. The performances of the proposed thermal system for the battery storage are compared with the fixed gain proportional integral derivative (PID) controller and the adaptive neural network controller based thermal systems in simulation and experiments.
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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) | 0.000 | 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 | 0.001 | 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".