A Low-Cost Battery Charger Usable with Sinusoidal Ripple-Current and Pulse Charging Algorithms for E-Bike Applications
Bibliographic record
Abstract
This paper focuses on the design of a low-cost battery charger usable under most advanced charging algorithms including sinusoidal ripple-current (SRC) and pulse charging strategies for e-bike applications. The designed charger is composed of an AC-DC converter at the frontend and a voltage-to-current (V-I) converter at the backend. The AC-DC flyback converter supplies DC voltage to the V-I converter. The V-I converter consists of two stages: a signal stage and a power stage. The signal stage includes an operational amplifier (Op-Amp) and an N-MOSFET to convert a voltage command to a reference signal for the power stage. Based on this reference signal, the power stage which consists of an Op-Amp and a P-MOSFET regulates the battery charging current. As a result, the designed charger is able to regulate the charging current in order to track any waveform of the input command. Hence, the SRC and pulse charging algorithms can be easily implemented using the designed charger. In order to validate the proposed approach, a charger for a 24 V/11.6 Ah Li-ion battery pack was designed and tested in laboratory. Both simulation and experimental results show that the designed battery charger can track the command under both SRC and pulse charging algorithms. In addition, both AC-DC and V-I converters require very low number of devices. Thus, the low cost of the proposed design makes it very attractive for practical applications such as e-bikes battery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".