Battery Monitoring System and Control Using LoRa Technology for Battery Behavior and Analysis
Bibliographic record
Abstract
Batteries are intricate electrochemical devices that demonstrate stochastic and nonlinear behavior contingent upon environmental and operational conditions; therefore, battery monitoring is an essential component throughout their application. This paper introduces a web-based battery monitoring and control system that utilizes Long Range (LoRa) communication technology, an element of Internet of Things (IoT), utilizing the ESP32 microcontroller. The system provides comprehensive online data in real-time through the integration of a multitude of sensors. The proposed system aims to overcome the constraints of current communication technology by capitalizing on the advantages of LoRa, a technology known for its efficient long-range and low-energy transmission, which renders it highly suitable for applications involving real-time monitoring. Furthermore, a control operation allows users to manage critical battery functions, including charging and discharging. The study performed a thorough experimental assessment of the proposed system across various operations, and the outcomes effectively corresponded with the primary objective and goals of the research. The system under consideration successfully facilitates remote monitoring and user control in real-time, provides long-term data visualization through data logging, and enables battery condition evaluation. The implementation of data logging was intended to optimize the utilization of future battery evaluation metrics, including State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). Consequently, the developed system is well-suited for numerous applications that demand efficient energy storage solutions, including Electric Vehicles (EVs) and renewable energy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".