Dynamic Simulation of Battery/Supercapacitor Hybrid Energy Storage System for the Electric Vehicles
Bibliographic record
Abstract
One of the most efficient options for enhancing energy use by electric vehicles is through hybridization using supercapacitors (SCs). A supercapacitor has many beneficial features especially its high efficiency, capacity to store large amounts of energy, a simpler charging system and quick delivery of charge. The objective of this paper was to highlight the benefits and demonstrate the feasibility of using SCs in combination with parallel battery in EVs by employing a modelling and simulation method. A semi-active topology which employed a single DC/DC converter was selected, and the performance of the battery/SC hybrid energy storage system (HESS) was evaluated for possible reduction in stress and extended battery life. The HESS was modelled based on generic battery, SC and converter models within Simscape Power Systems in Matlab-Simulink and ADVISOR. The HESS model was validated by data from the literature and showed a good compatibility. This implies that the model used in the present study is reliable and have a high probability of deriving an accurate prediction of the HESS performance. Dynamic simulations were performed for Tesla S70 electric car. The results relating to hybridization showed a significant reduction in battery charge. The SC power contribution and the range extension in the HESS was estimated to be in average 21.5% and 80 km for the USC06 driving cycle, respectively. The simulation results presented a range of verified benefits attributed to the HESS: by deploying transient currents during acceleration and deceleration which greatly reduces battery stress, there is significant enhancement of system performance; an appreciable reduction in the number of cycles/year has a direct positive impact on battery aging process; there is a striking increase in vehicle range; and finally, it provides insulation for the battery pack at very cold ambient air temperatures. Moreover, the hybridization could allow reducing the size of the primary power source or the EV battery.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".