Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling
Bibliographic record
Abstract
Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. This paper presents a metaheuristic-based approach parallelized on multicore processors (CPU) and graphics processing units (GPU) to optimize the scheduling of EV charging in a single smart parking lot. The proposed method uses a particle swarm optimization algorithm that takes as input the arrival time, the departure time, and the power demand of the vehicles and produces an optimized charging schedule for all vehicles in the parking lot, which minimizes the overall charging cost while respecting the chargers’ capacity and the parking lot feeder capacity. The algorithm exploits task-level parallelism for the multicore CPU implementation and data-level parallelism for the GPU implementation. The proposed algorithm is tested in simulation on parking lots containing 20 to 500 EVs. The parallel implementation on CPUs provides a speedup of 7.1x, while the implementation on a GPU provides a speedup of up to 247.6x. The parallel implementation on a GPU is able to optimize the charging schedule for a 20-EV parking lot in 0.87 s and a 500-EV lot in just under 30 s. These runtimes allow for real-time computation when a vehicle arrives at the parking lot or when the electricity cost profile changes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".