Electrochemical and Electrostatic Energy Storage and Management Systems for Electric Drive Vehicles: State-of-the-Art Review and Future Trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, increased emissions regulations and a push for less dependence on fossil fuels are factors that have enticed a growth in the market share of alternative energy vehicles. Readily available energy storage systems (ESSs) pose a challenge for the mass market penetration of hybrid electric vehicles (HEVs), plug-in HEVs, and EVs. This is mainly due to the high cost of ESS available today. However, tremendous research efforts are going into reducing the cost of these storage devices, increasing their lifespan, and improving their energy density. This paper aims to give an overview of the current state of readily available battery and ultracapacitor (UC) technologies as well as a look ahead toward promising advanced battery chemistries and next generation ESS. Energy management systems and various battery balancing configurations are discussed in addition to battery state/parameter estimation and protection mechanisms. Finally, hybrid ESSs (HESS) are reviewed as a mitigation strategy to the shortcomings of traditional battery and UC technologies. Consideration is given to the combination of advanced battery chemistries with UCs to portray HESS performance, which can meet and exceed the performance of current ESS technologies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it