Lithium Ion SuperPolymer® High-Performance Battery for Ultra-Safe, Long-Range ZEVs, HEVs, and PHEVs
Bibliographic record
Abstract
Electrovaya’s Lithium Ion SuperPolymer® based battery is a cost-effective solution for long-range, ultrasafe zero-emission (battery electric) and low-emission (plug-in hybrid) vehicles. Optimized for transportation, Electrovaya has developed fully integrated power system solutions with large-format cells, large-format modules, and integrated an intelligent battery management system for safe and effective scale-up. Electrovaya’s proprietary SuperPolymer® technology is independent of the composition of the positive electrode active material and so multiple chemistry solutions are available, including the MN-Series, a Lithiated Manganese Oxide based system, with up to 50% higher energy density and comparable safety characteristics to Electrovaya’s Phosphate-Series solution. This paper details the design approach and recent test results of Electrovaya’s solutions for (1) plug-in hybrids, with its program with New York State on a Ford Escape platform; (2) passenger vehicles, as demonstrated in a 30kWh ZEV in Norway; (3) fleet vehicles, such as an 80kWh delivery vehicle in partnership with Unicell, Purolator and the Canadian Government; and (4) off-road vehicles, such as its project with New York State Parks.Electrovaya’s scalable power train solution is easily tailored to the demands of passenger, fleet, off-road, and heavy-duty applications. This solution is cost-competitive to ICE-based vehicles and superior in performance, safety and operating cost. Electrovaya’s SuperPolymer® technology gives superior safety and performance and is exceptionally suited to large format systems necessary for transportation applications.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".