Hardware implementation of an improved control strategy for battery–supercapacitor hybrid system in electric vehicles
Bibliographic record
Abstract
This study deals with the implementation of an efficient control strategy using battery–supercapacitor for an electric vehicle driven by a permanent‐magnet synchronous motor. The whole system consists of two parts: the energy management system and the traction system. The energy management system is mainly composed of a fuzzy‐Lyapunov controller used to regulate both the current sources and the DC‐bus voltage. For the traction system, direct torque control based on 12 sectors drive is used for the control of the motor to ensure both decoupled flux and torque with low ripple compared with the conventional Direct Torque Control (DTC). To make a comparative study for the energy management system, two strategies of energy management have been implemented. The first strategy does not include the regulation of the supercapacitor voltage, whereas the second one is based on the regulation of the supercapacitor voltage to protect it from deep discharge and avoid short circuit. The experimental tests were implemented using two dSPACE 1104 implementation boards. The results show that the system under the second energy control strategy works perfectly and verifies the effectiveness of the proposed control technique.
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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".