Electrochemical Cells and Storage Technologies to Increase Renewable Energy Share in Cold Climate Conditions—A Critical Assessment
Bibliographic record
Abstract
The energy efficiency of a renewable energy system is inextricably linked to the energy storage technologies used in conjunction with it. The most extensively utilized energy storage technology for all purposes is electrochemical storage batteries, which have grown more popular over time because of their extended life, high working voltage, and low self-discharge rate. However, these batteries cannot withstand the very low temperatures encountered in cold regions, even with these very promising technical characteristics. The cold northern temperatures affect the batteries’ electromotive force and thus decrease their storage capacity. In addition, they affect the conductivity of the electrolyte and the kinetics of electrochemical reactions, thus influencing the capacity and speed of electrons in the electrolyte. In this article, which is intended as a literature review, we first describe the technical characteristics of charge–discharge rate of different electrochemical storage techniques and their variations with temperature. Then, new approaches used to adapt these electrochemical storage techniques to cold climates are presented. We also conduct a comparative study between the different electrochemical storage techniques regarding their performance in the harsh climatic conditions of the Canadian North.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".