Performance and Comparative Analysis of Math Function Based Controller Combined with PID and PI for Smooth Transition of Energy Sources
Bibliographic record
Abstract
Hybrid Energy Storage System (HESS) with battery and ultra-capacitor (UC) gives good results for Hybrid Electric Vehicle (HEV)/Electric Vehicle (EV) application due to its inherent high energy and high power densities.High power capability of UC can be utilized during cold starting and sudden requirement of the EV.Normal power need can be supplied by the battery itself only.The main obstacle with HESS based EVs is the transition between battery and UC.The smooth transition plays a key role in improving battery life.The main aim of this work is to develop a control technique for automatic switching between energy sources corresponding to the speed of the motor.In the proposed control action, motor speed plays a major role in switch the energy sources in HESS.To attain the objective, another controller has been designed with four math functions corresponding to the speed of the motor termed as Math Function Based (MFB) controller.Thereafter the designed MFB controller combined with a conventional PI controller applied to the entire circuit at different load conditions.In the same way, MFB with PID controller also applied to the circuit.Finally, comparative analysis has been done between two hybrid controllers.The MATLAB/Simulink results of MFB with PI and MFB with PID has been attained and also compared, discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".