Energy Recovery and Energy Harvesting in Electric and Fuel Cell Vehicles, a Review of Recent Advances
Bibliographic record
Abstract
This review article examines the crucial role of energy harvesting and energy recovery in the design of battery electric vehicles (BEVs) and fuel cell hybrid electric vehicles (FCHEVs) as these vehicles have limited onboard power sources. Harvesting energy and recovering energy from onboard systems can significantly improve energy efficiency, increase range, and reduce fuel consumption. The latest advances in vehicular energy recovery and harvesting, including regenerative braking, regenerative suspension, solar and wind energy harvesting, and other recovery methods are studied and the impact of the energy storage system and powertrain architecture on energy harvesting is investigated. Regenerative brake control strategies and driver behaviour’s effect on energy recovery are reviewed, and the potential of energy harvesting in electric vehicles is discussed, including experimental and low-power harvesting methods. The importance of using perception and navigation technologies in autonomous vehicles to enhance energy efficiency is highlighted. The article identifies critical research gaps, challenges, and future directions for research in this field. This review stands out from previous papers by covering overlooked subjects such as driver behaviour and deceleration planning in autonomous vehicles, low-power harvesting methods, and experimental techniques applicable to electric vehicles.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".