Designing Intelligent Energy Management and Cost-effective Data Acquisition for Vehicular Solar Idle Reduction Systems
Bibliographic record
Abstract
In this study, an innovative energy management system (EMS) employing the promising reinforcement learning (RL) method is proposed. The EMS intelligently administrates the power flow between the main battery which is fed through the alternator and a solar-powered auxiliary battery which is used for the vehicle idle time reduction via providing energy for auxiliary loads which force the engine to be running, although the service vehicle is stopped. RL, which is an exquisite artificial intelligence technique, endeavors to offer a sub-optimal performance for this control problem compared to the really time consuming Dynamic Programming approach, which determines the optimal solution through exhaustive search. \nA service vehicle is modeled in the Matlab/Simulink environment. Different parts of the model are described in detail, and the dynamics of the considered vehicle are discussed. The simulation results express a better functionality compared to an existing rule-based controller and the idled engine case, turning the proposed RL-based EMS into an effective method for implementation in vehicular solar idle reduction (SIR) systems. Double DQN is also utilized to come up with the continuous observation space. The results are showing that Deep-RL can be a promising method in control tasks like the EMS of vehicular systems. \nFurthermore, a cost-effective and efficient data acquisition system is designed, tested, and implemented using the renowned Raspberry Pi board, and some sensors to collect voltage, current, and temperature data. The required electrical enclosures are also designed to keep the whole package safe. The validation of the system results is done and the process is discussed in detail. This data acquisition system can be employed to read the required information from vehicle and its loads, in order that the intelligent EMS system can wisely decide which action to take in a real-time manner.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".