Battery Swap Scheduling Eval

---
name: battery-swap-scheduling-eval
description: Evaluates a genetic algorithm enhanced with an LRU strategy for estimating battery swap demand and optimizing 24-hour charging schedules. It probes the algorithm's ability to minimize charging costs while maintaining high user satisfaction and computational efficiency under real-world demand fluctuations. Use when the user wants to benchmark on ST-EVCDP series, UrbanEV series, or asks about evaluating this task. Reports optimization rate (r_opt).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2504.07453
  bibtex_key: [REDACTED]
  confidence: high
---

# battery-swap-scheduling-eval

> Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System — Li et al. (2025) (arXiv:2504.07453, 2025)

## What this evaluates

Evaluates a genetic algorithm enhanced with an LRU strategy for estimating battery swap demand and optimizing 24-hour charging schedules. It probes the algorithm's ability to minimize charging costs while maintaining high user satisfaction and computational efficiency under real-world demand fluctuations.

## Datasets

- **ST-EVCDP series** — total ?; splits: test (-1)
- **UrbanEV series** — total ?; splits: test (-1)

## Metrics

- `optimization rate (r_opt)` **(primary)** — range: percent
  - Percentage reduction in total charging cost compared to the immediate swap-and-charge baseline: r_opt = (C_is - C_ours) / C_is * 100%. Higher values indicate greater cost savings.
- `user satisfaction (γ)` — range: percent
  - Percentage of total battery swap demands successfully fulfilled by the optimized charging schedule. Higher values indicate better service coverage.
- `average iteration time (τ)` — range: seconds
  - Mean computational time required per iteration of the GA-EVLRU algorithm. Lower values indicate higher computational efficiency.

## Input / output format

**Input**: Hourly battery swap demand sequences and electricity price profiles for a specific region, along with constraints on battery types (Type A/B) and charging station capacity limits.

**Output**: A 24-hour charging schedule specifying the number of batteries to charge per hour for each type, plus computed metrics: total cost, optimization rate, user satisfaction, and iteration time.

## Scoring recipe

```python
def evaluate_schedule(schedule, demand, prices, baseline_cost):
    total_cost = sum(schedule[h] * prices[h] for h in range(24))
    r_opt = (baseline_cost - total_cost) / baseline_cost * 100
    satisfied = min(sum(schedule), sum(demand))
    gamma = satisfied / sum(demand) * 100
    return r_opt, gamma, total_cost
```

## Common pitfalls

- The baseline is a naive 'immediate swap-and-charge' strategy rather than an optimized solver, which may artificially inflate the reported optimization gains.
- Evaluation is restricted to only 10 sampled regions from the full datasets due to computational budget constraints, limiting geographic generalizability.
- Demand estimation metrics (S_m, P_m, O_m) assess curve shape fidelity and periodicity rather than point-wise prediction accuracy against ground truth swap events.

## Evidence (verbatim from paper)

> Table 3. Results of GA-EVLRU on two datasets. We use ↑ for the higher the better and ↓ for the reverse. C_is represents the cost of the immediate swap-and-charge strategy; C_ours represents the cost of GA-EVLRU; r_opt represents the optimization rate; γ represents the user satisfaction level. τ represents the average iteration time per iteration.

## Citation

```bibtex
@misc{li2025probability,
  title={Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System},
  author={Li et al. (2025)},
  year={2025},
  note={arXiv:2504.07453}
}
```

- arXiv: 2504.07453