Development of Utility Friendly Olivine Based ESS in Esstalion Technologies
Bibliographic record
Abstract
One of the promising approaches for striking a balance of realizing sustainable society and maximizing utility’s profit is to apply energy storage system (ESS). There’re many use cases proposed (1), such as reserve, regulation, peak shaving, time shift, load following, smoothing, and minimum emission. To answer these demands, the development of a battery with high rate of charging and discharging, a longer cycle life and safe is imperative. Esstalion Technologies Inc. was established in 2014 as a joint venture company between Sony Corporation and Hydro-Québec.(2) We are the first corporate joint venture between battery manufacturer and utility. Since then, we’ve developed utility friendly ESS based on lithium ion battery technology for grid scale utilization. Since we put our importance on safety and long-life, our core technology is olivine based material which is patented by Hydro Québec and commercialise by Sony as Fortelion. (3) We’ve started in field testing using 1.2 MWh ESS in 2016. In Esstalion, we try to bring innovation by gathering “multi-wisdom” in ONE ESSTALION team, doing the new material research, BMS/EMS development and ROI calculation etc. We propose a brief review of our technologies and we will show an example of our efforts to enhance the key properties of the Li-ion battery. (1) Pacific Northwest National Laboratory, Protocol for Uniformly Measuring and Expressing the Performance of Energy Storage Systems (2) Press Release, Establishment of Esstalion Technologies, Inc., a joint venture between Hydro-Québec and Sony, 2014 (3) News Release, Sony Launches High-power, Long-life Lithium Ion Secondary Battery Using Olivine-type Lithium Iron Phosphate as the Cathode Material, 2009
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".