Time-Varying Current Charging Strategies for Lithium-Ion Batteries in Electric Vehicles: Trends, Challenges, and Opportunities
Bibliographic record
Abstract
The global push for net zero emissions (NZE) by 2050 has accelerated the adoption of electric vehicles (EVs), supported by rapid advances in lithium ion battery (LiB) technologies, supportive policies and market incentives. Despite progress in addressing challenges such as high costs and range anxiety, the limited lifetime of LiBs remains a critical barrier to widespread EV adoption. This paper explores time-varying current (TVC) charging strategies, particularly pulse current charging (PCC), as promising alternatives or complements to the conventional constant current-constant voltage (CC-CV) method. It reviews recent trends in EV adoption, LiB chemistries, and state-of-health (SoH) estimation methods, including experimental, adaptive, and data-driven approaches. The analysis highlights the potential of PCC strategies to extend battery lifetime, enabling opportunities in vehicle-to-grid (V2G) services and shared mobility systems. By addressing current challenges and leveraging these opportunities, TVC charging methods offer promising solutions to extend the useful life of LiB and support a sustainable and decarbonized transportation ecosystem.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".