Publication 1
A Survey of Coordination Techniques for EV-EV Energy Trading
Peer-to-peer energy trading for electric vehicles (EVs) offers a prospective solution for overcoming a major barrier to EV adoption through increased energy supply and distribution. It leverages the maturing technologies of bidirectional charging capacity of EVs and wireless charging pads (WCP), which is crucial for expanding charging stations (CS) beyond central urban areas and, thus, for increased driver convenience. However, EV-EV trading carries key differences compared to that of EV-CS trading. Mobile vehicles within convenient proximity to WCPs need to be matched for energy trading. Each carries distinct journey requirements, navigation choices across traffic routes with varying traffic densities, diminishing energy levels while mobile, and driver biases of where to 'turn and stop' for different areas and times of the day. To reduce driver inconvenience and minimize latency under these variables of uncertainty, capabilities are required to support EV drivers to coordinate their journeys and make decisions about where to consume or provide energy. In this paper, we provide a state-of-the-art survey of technical capabilities concerned with coordination aspects of EV-EV trading, i.e., prediction, optimization, scheduling, and data sharing. To guide the survey analysis of existing computational techniques supporting these capabilities for the purposes of EV-EV trading, we define a framework, comprising salient coordination dimensions and their various aspects. This coordination framework serves as an analytical lens to identify research gaps in the current development. Given the research gaps found through our survey and requirements for EV-EV trading, we recommend important areas for future research directions: for predictions given EV mobility contexts, enriched multidimensional data and privacy-preserving techniques are critical; for optimization, multi-objective functions and constraints need to reflect EV mobility contexts while multistep optimization strategies are required for the scale of EV-EV matching, including calculating optimal spatial scopes prior to applying matching algorithms; for network-wide scheduling of EV-EV trading, hybrid scheduling strategies are recommended; and for data sharing, robustness given the scale of the distribution and mobile setting presents new research challenges.
Key Words: coordination techniques, data sharing, digital ledger technology, energy trading, ev-ev energy trading, matching, optimization, prediction, provider-consumer matching, scheduling
Publication 2
EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading
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.
Key Words: Electric vehicle energy trading, Charging nodes recommendation, Learning-to-rank, Gradient boosted ranking models, Electric vehicle mobility data, Mobility-aware energy trading.
Awards
Sessional Accomplishment Award from School of Information Systems, 2025
Poster Presentation
Attended and presented a poster at the Maths & IT showcase at X block, QUT, August 1, 2025
Doctoral Consortium
Attended the Doctoral Consortium in the School of Information Systems- 2022, QUT, Australia.
Participated in the Doctoral Consortium in the School of Information Systems- 2023, QUT, Australia. - presented research work to the experts.
Participated in the Doctoral Consortium in the School of Information Systems- 2024, QUT, Australia. - presented research work to the experts.
Confirmation of Candidature
Presented research work to panel members - 8th December 2023
Confirmation of Stage 2
Submitted research proposal to QUT - 22 November 2022
Participated in the Decarbonisation & the UN Sustainable Development Goals, Higher Degree Research Design Sprint 2023 for one week.
Participated in the Event Management Workshop, organized by SE Grant - Event Management Workshop, 8 Feb 2024.
Participated in a technical writing and formalization workshop, organized by the School of Information Systems, January 2025
Participated as a volunteer at School Outreach Program, 2024, organized by School of Information System.
Participated as a volunteer in the Doctoral Consortium in the School of Information Systems- 2024, QUT, Australia .
Worked as an executive member at QUT Bangladeshi Association in 2023.
Worked as a general secretary at QUT Bangladeshi Association in 2024.
Working as a President at QUT Bangladeshi Association in 2025.
IEEE Graduate Student Membership, Queensland Section, 2025. Membership ID: 92992257
Member, Toastmaster QUT, 2025