Charging Decision Optimization Strategy for Shared Autonomous Electric Vehicles Considering Multi-Objective Conflicts: An Integrated Solution Process Combining Multi-Agent Simulation Model and Genetic Algorithm
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Bibliographic record
Abstract
There is a lack of systematic research on the behavioral design of charging decision-making for Shared Autonomous Electric Vehicles (ASEVs), and the thresholds of “when to charge and where to charge” have not been clarified. Therefore, this paper investigates the optimization of charging decisions of SAEVs and the impact of different decision-making objectives to provide theoretical support and practical guidance for intelligent operation. A multi-agent simulation model (which accurately simulates complex interaction systems) is constructed to simulate the operation and charging behavior of SAEVs. Four charging decision optimization objective functions are defined, and a weighted multi-objective optimization method is adopted. A comprehensive solution process combining the multi-agent simulation model and genetic algorithm (efficiently solving complex objective optimization problems) is applied to approximate the global optimal solution among 35 scenarios through 100 iterative runs. In this paper, factors such as passenger demand (e.g., average remaining battery power, demand response time) and operator demand (e.g., empty vehicle mileage, charging cost) are considered, and the impacts of different objectives and decision variables are analyzed. The optimization results show that (1) when a single optimization objective is selected, minimizing the total charging cost effectively balances the overall fleet operation; (2) there are trade-offs between different objectives, such as the conflict between the remaining battery power and charging cost, and the balance between the demand response time and the empty vehicle mileage; and (3) in order to satisfy the operational requirements, the weight distribution, charging probability, stopping probability, and recommended battery power should be adjusted. In conclusion, this study provides optimal charging decision strategies for the intelligent operation of SAEVs in different scenarios, which can optimize target weights and charging parameters, and achieve dynamic, balanced fleet management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it