Development of Preheating and Power Inverting Systems for Lithium-Ion Batteries
Bibliographic record
Abstract
A novel short-circuit self-heating (SCSH) control system was developed in this thesis to achieve the preheating of lithium-ion (Li-ion) batteries operated in extremely cold weather (< -30°C).The proposed system relies on the internal resistance of batteries and the short circuit current to heat up batteries using Joule heating.Experiments show that the SCSH control system can heat up the commercial Panasonic 18650 Li-ion batteries from -30°C to 0°C in 43 seconds, with less than 5 percent of the battery capacity consumed.The proposed heating system outperformed both external convective air heating and alternating current (AC) heating, in terms of heating time and energy consumption.Furthermore, a DC to AC battery power inverter was developed to implement the AC heating and to make the battery pack available for household appliances.This inverter employs a microcontroller using the direct pulse width modulation (DPWM) technique.The inverter achieves power output at various frequencies through programming, without changing the design of the circuit board.The optimal frequency ratio can be obtained theoretically, validated through MATLAB simulation, and was further examined through experimentation.The selected frequency ratio enables the DPWM signals to stimulate the designed inverter to produce high quality sinusoidal voltage.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".