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EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading

Md Mahfujur Rahmana, Alistair Barros, Raja Jurdak, Darshika Koggalahewa

Abstract

Peer-to-peer energy trading among electric vehicles (EVs) has been increasingly studied as a promising solution for improving supply-side resilience under growing charging demand and constrained charging infrastructure. While prior studies on EV-EV energy trading and related EV research have largely focused on transaction management or isolated mobility prediction tasks, the problem of identifying which charging nodes are more suitable for EV-EV trading in journey contexts remains open. We address this gap by formulating next charging nodes recommendation as a learning-to-rank problem, where each EV decision event is associated with a set of candidate charging locations. We propose a supervised ranking framework applied to a large-scale urban EV mobility dataset comprising millions of journey records and multidimensional EV trading-related features, including EV energy level, trading role, distance to charging locations, charging speed, and temporal station popularity. To account for uncertainty arising from the mobility of both energy providers and consumers, as well as the presence of multiple viable charging nodes at a decision point, we employ probabilistic relevance refinement to generate graded labels for ranking. We evaluate gradient-boosted learning-to-rank models, including LightGBM, XGBoost, and CatBoost, on EV journey records enriched with candidate charging nodes. Experimental results show that LightGBM consistently achieves the strongest ranking performance across standard metrics, including NDCG@k, Recall@k, and MRR, with particularly strong early-ranking quality, reflected in the highest NDCG@1 (0.9795) and MRR (0.9990). These results highlight the effectiveness of uncertainty-aware learning-to-rank for charging node recommendation and support improved coordination and matching in decentralized EV-EV energy trading systems.

EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading

Abstract

Peer-to-peer energy trading among electric vehicles (EVs) has been increasingly studied as a promising solution for improving supply-side resilience under growing charging demand and constrained charging infrastructure. While prior studies on EV-EV energy trading and related EV research have largely focused on transaction management or isolated mobility prediction tasks, the problem of identifying which charging nodes are more suitable for EV-EV trading in journey contexts remains open. We address this gap by formulating next charging nodes recommendation as a learning-to-rank problem, where each EV decision event is associated with a set of candidate charging locations. We propose a supervised ranking framework applied to a large-scale urban EV mobility dataset comprising millions of journey records and multidimensional EV trading-related features, including EV energy level, trading role, distance to charging locations, charging speed, and temporal station popularity. To account for uncertainty arising from the mobility of both energy providers and consumers, as well as the presence of multiple viable charging nodes at a decision point, we employ probabilistic relevance refinement to generate graded labels for ranking. We evaluate gradient-boosted learning-to-rank models, including LightGBM, XGBoost, and CatBoost, on EV journey records enriched with candidate charging nodes. Experimental results show that LightGBM consistently achieves the strongest ranking performance across standard metrics, including NDCG@k, Recall@k, and MRR, with particularly strong early-ranking quality, reflected in the highest NDCG@1 (0.9795) and MRR (0.9990). These results highlight the effectiveness of uncertainty-aware learning-to-rank for charging node recommendation and support improved coordination and matching in decentralized EV-EV energy trading systems.

Paper Structure

This paper contains 33 sections, 39 equations, 12 figures, 8 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. EV charging supply and demand prediction
  4. EV mobility prediction
  5. Charging location recommendation system for EVs
  6. Features used for mobility prediction
  7. Research issues
  8. Rationale for future state recommendation
  9. Study design
  10. Problem formulation
  11. Research methodology
  12. Overall system pipeline
  13. Data acquisition and preliminary analysis
  14. Label selection
  15. Fuzzy-weighted TOPSIS for station suitability estimation
  16. ...and 18 more sections

Figures (12)

  • Figure 1: Overall process for the EV--EV trading datasets generation.
  • Figure 2: Distribution of trips per EV model shown as a bar plot, ordered from most to least frequent model.
  • Figure 3: Distribution of unique EVs per model (distinct EV IDs), ordered by model frequency.
  • Figure 4: Spatial distribution of EV locations by energy level (20–40%, 40–60%, 60–80%, 80–100%) for 30-minute windows, high peak (08:30, 17:30) and off-peak (06:00, 22:00) periods; legends report per energy level counts.
  • Figure 5: Label generation pipeline. Unlabeled EV decision events from the NextTrade-EV dataset are first evaluated using fuzzy TOPSIS to compute multi-criteria station suitability scores. These scores are subsequently refined through EM-based probabilistic modeling to capture latent contextual uncertainty. The resulting soft relevance scores are discretized into graded relevance labels, yielding a weakly labeled dataset suitable for learning-to-rank without requiring observed station selection data.
  • ...and 7 more figures