Power-Electronics-Based Solutions for Plug-in Hybrid Electric Vehicle Energy Storage and Management Systems
Bibliographic record
Abstract
Batteries, ultracapacitors (UCs), and fuel cells are widely being proposed for electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) as an electric power source or an energy storage unit. In general, the design of an intelligent control strategy for coordinated power distribution is a critical issue for UC-supported PHEV power systems. Implementation of several control methods has been presented in the past, with the goal of improving battery life and overall vehicle efficiency. It is clear that the control objectives vary with respect to vehicle velocity, power demand, and state of charge of both the batteries and UCs. Hence, an optimal control strategy design is the most critical aspect of an all-electric/plug-in hybrid electric vehicle operational characteristic. Although much effort has been made to improve the life of PHEV energy storage systems (ESSs), including research on energy storage device chemistries, this paper, on the contrary, highlights the fact that the fundamental problem lies within the design of power-electronics-based energy-management converters and the development of smarter control algorithms. This paper initially discusses battery and UC characteristics and then goes on to provide a detailed comparison of various proposed control strategies and proposes the use of precise power electronic converter topologies. Finally, this paper summarizes the benefits of the various techniques and suggests the most viable solutions for on-board power management, more specific to PHEVs with multiple/hybrid ESSs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".