From Innovation To Market: The Role Of Energy Management, Smart Charging, And Renewable Integration In The Evolution Of Hybrid And Electric Vehicles.
Bibliographic record
Abstract
The rapid evolution of hybrid and electric vehicle (EV) technologies has necessitated advancements in energy management strategies, powertrain optimization, and sustainable charging infrastructure. This review consolidates recent research contributions in hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell hybrid electric vehicles (FCHEVs), focusing on thermal management, battery optimization, and energy-efficient charging systems. Studies highlight intelligent energy management strategies incorporating real-time speed profiles, particle swarm optimization, and meta-model-based techniques to enhance vehicle performance and reduce emissions. Thermal management systems integrating hybrid control techniques have demonstrated improved efficiency in battery and power electronics cooling. Additionally, research on powertrain configurations, including series, parallel, and power-split architectures, has emphasized the role of advanced control algorithms in maximizing fuel economy and extending battery life. Sustainable charging infrastructures leveraging renewable energy sources, such as solar-powered charging stations, are gaining traction, with optimization models designed for efficient energy distribution. Furthermore, the long-term impact of battery aging and vehicle performance degradation on emissions and cost-effectiveness is examined. Comparative studies assessing hybridization benefits in terms of lifecycle emissions and fuel economy provide insights into the economic and environmental feasibility of different powertrain technologies. This review aims to present a comprehensive synthesis of contemporary advancements and challenges in hybrid and electric vehicle technologies, providing a foundation for future research directions toward sustainable and energy-efficient transportation solutions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".