Optimizing Lithium-Ion Battery Performance and Safety for E-Bikes: A Review of Machine Learning-Driven Battery Management Systems
Bibliographic record
Abstract
The growing demand for sustainable transportation has positioned electric bikes (e-bikes) as a key solution, with lithium-ion batteries (LIBs) critical for their performance and reliability. This review provides a comprehensive examination of recent advancements in optimizing LIBs for e-bikes, focusing on integrating machine learning (ML) into Battery Management Systems (BMS) and developing fast-charging solutions. The review explores state-of-the-art machine learning models used for State of Charge (SOC) and State of Health (SOH) estimation, significantly improving prediction accuracy, adaptability, and battery safety under real-world conditions. Fast-charging technologies, essential for enhancing the user experience of e-bikes, are also evaluated, focusing on balancing rapid charging and minimizing degradation. Despite these advancements, challenges remain in real-time system integration, computational efficiency, and thermal management. The review highlights future research opportunities, including developing lightweight, adaptive AI models and novel materials for improving energy density and safety. This work aims to advance the design and application of LIBs in e-bikes, contributing to the broader adoption of sustainable, electric transportation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".