Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System

• URL: http://arxiv.org/abs/2504.07453v2
• Date: Thu, 01 May 2025 13:57:36 GMT
• Title: Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System
• Abstract summary: This research proposes a probability estimation model based on charging pile data and constructs nine scenario-specific battery swap demand datasets.<n>It incorporates a guided search mechanism, which effectively enhances the global optimization capability.<n>When benchmarked against immediate swap-and-charge strategy, our algorithm achieves a peak cost reduction of 13.96%.
• Score: 2.845879685273271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To address the challenges of limited Battery Swap Stations datasets, high operational costs, and fluctuating user charging demand, this research proposes a probability estimation model based on charging pile data and constructs nine scenario-specific battery swap demand datasets. In addition, this study combines Least Recently Used strategy with Genetic Algorithm and incorporates a guided search mechanism, which effectively enhances the global optimization capability. Thus, a dual-factor decision-making based charging schedule optimization system is constructed. Experimental results show that the constructed datasets exhibit stable trend characteristics, adhering to 24-hour and 168-hour periodicity patterns, with outlier ratios consistently below 3.26%, confirming data validity. Compared to baseline, the improved algorithm achieves better fitness individuals in 80% of test regions under the same iterations. When benchmarked against immediate swap-and-charge strategy, our algorithm achieves a peak cost reduction of 13.96%. Moreover, peak user satisfaction reaches 98.57%, while the average iteration time remains below 0.6 seconds, demonstrating good computational efficiency. The complete datasets and optimization algorithm are open-sourced at https://github.com/qingshufan/GA-EVLRU.

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