Globally Optimal Energy Management in a Battery-Ultracapacitor Electric Vehicle
Bibliographic record
Abstract
Hybridization of batteries with other energy storage systems looks like a promising solution for decreasing battery size and cost and extending its lifetime as well as electric vehicles' (EVs) driving range. This paper selected the Chevy Spark 2015 battery electric vehicle (BEV) integrating the ultracapacitor (UC) model as the investigated case study. Firstly, a dynamic model of the battery pack is developed in which its parameters, including the internal resistance, open-circuit voltage, and capacity, are adaptively updated based on the battery pack's instant temperature and state of charge (SOC). Then, two different energy management strategies (EMSs), i.e., a rule-based method and dynamic programming (DP), are implemented to allocate power between the energy sources. The EMSs are run over a standard driving cycle, and the hybrid energy storage system's (HESS's) outputs, including battery SOC, UC state of voltage (SOV), and the battery's capacity loss, are compared with the same vehicle equipped with the battery pack only. The simulation results show that DP can decrease SOC depletion by 4% and minimize the battery degradation by 39.2% on average compared to the proposed rule-based method for one of the standard driving cycles and three ambient temperatures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".