Dynamic Control of Traction Motor for EV Fed via Dual Source Inverter with a Two Battery System
Bibliographic record
Abstract
An electric vehicle uses multiple energy-storage systems to power the traction motor. Dual-source inverters (DSIs) are used for single-stage power conversion by skipping the dc/dc boost converter stage; therefore, eliminating the passive magnetic storing element which improves the overall efficiency of the drive; moreover, multiple energy-storage systems improve the power density of the system. This article discusses the fine control of a traction motor from zero speed to rated speed supplied through a dual-source inverter. Field-oriented control with space vector modulation technique is applied to achieve closed-loop control. Two dc sources are used, one having a higher-voltage battery and one a lower-voltage battery. The higher-voltage battery is the main battery which supplies power to the traction motor, whereas the lower-voltage battery supplies power to supplementary loads of the EV. This article presents improved dynamic behaviour of an induction-motor-driven EV fed from a dual-source inverter using modified closed-loop field-oriented control with space vector modulation. The improvement includes reduced control complexity due to space vector modulation and achieving the option of EV operation in an emergent situation using the same converter and control system. The simulated performance of the presented system is obtained in MATLAB/Simulink. A step-down experimental prototype is used for verification of effective control of the induction motor as the EV is under constant torque variable speed operation with real-time parameters such as power, power factor, current harmonics, and voltage/current stresses across the switch using two batteries individually.
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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 | 0.000 | 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".