Optimal Allocation of Electric Vehicles Charging Stations in Commercial Parking Lots: A Mixed-Integer Nonlinear Programming Approach
Bibliographic record
Abstract
This study presents a mixed-integer nonlinear programming (MINLP) framework to optimize the allocation of electric vehicle charging stations (EVCSs) in existing indoor parking facilities. The model minimizes total life-cycle cost by jointly determining charger types and placements while accounting for spatial feasibility and investment constraints. A hybrid search method that combines complete enumeration with dynamic programming is applied to identify the least-cost configuration within geometric and electrical limitations. The results show that configurations combining dual- and quad-port chargers outperform single-port layouts by reducing redundant electrical and installation costs. The analysis confirms that integrating life-cycle costing with spatial feasibility yields a practical decision-support tool for property owners seeking to expand charging capacity within existing facilities. Overall, the framework demonstrates that cost-efficient retrofitting of EV charging infrastructure can be achieved without additional land development, supporting broader sustainability objectives and promoting low-carbon mobility. Future research will extend the model to multiple facility layouts and incorporate sensitivity and uncertainty analyses to evaluate robustness under varying geometric and economic conditions. The findings of this paper provide a practical foundation for future planning studies and demonstrate how cost-optimized retrofit strategies can support the scalable expansion of EV charging infrastructure in existing facilities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".