Artificial Neural Networks Based Power Management for a Battery/Supercapacitor and Integrated Photovoltaic Hybrid Storage System for Electric Vehicles
Bibliographic record
Abstract
Integrating solar energy in electric vehicles (EV) is expected to play a dominant role in the decarbonization of the transportation sector as well as reducing the charging costs.These integrated photovoltaic automobiles are particularly adapted for urban driving that is to say the driving range is limited.On that account, the use of a feasible energy storage system is necessary to boost the driving mileage.This paper uses a hybrid energy system, containing a battery as the main storage device and a supercapacitor (SC) as a backup.This combination was suggested in order to negate the former's deficiencies.The use of a hybrid energy source leads to the necessity of introducing a robust power management strategy to guarantee the optimal power flow in electric vehicle components.An artificial neural network is then trained with model calculation for the power management approach for the traction chain.The results of the simulation indicate that the proposed system is efficient in improving the energy management of the vehicle.Moreover, the system's simplicity could potentially make it easier to implement in real-time using a DSP or a DSPACE platform.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".