Electric Vehicles Under Low Temperatures: A Review on Battery Performance, Charging Needs, and Power Grid Impacts
Bibliographic record
Abstract
Electric vehicles (EVs) are gaining mainstream adoption as more countries introduce net-zero carbon targets for the near future. Lithium-ion (Li-ion) batteries, the most commonly used energy storage technology in EVs, are temperature sensitive, and their performance degradates at low operating temperatures due to increased internal resistance. The existing literature on EV-power grid studies assumes that EVs are used under “perfect temperatures" (e.g. 21 Celsius) and temperature-related issues are ignored. In addition, most of the countries/regions with high EV penetration (e.g. Norway, Canada, northern parts of the US and China, etc.) experience harsh cold months, making it extremely critical to understand EV performance and consequently their impacts on the electrical power networks. In this paper, we present a systematic review of the literature that considers the combined investigation of Li-ion battery technology and power networks, with a focus on their operation under suboptimal weather conditions. More specifically, we review: (i) the impact of low temperatures on the electrochemical performance of EV batteries in parking, charging and driving modes, (ii) the challenges experienced by EVs during charging and associated performance degradation, and (iii) the additional impacts of EV charging on the power networks. Our analysis shows that there are serious research gaps in literature and industry applications, which may hinder mass EV adoption and cause delays in charging station roll-out.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".