Periodic Reporting for period 1 - EVOLVE (Electric Vehicles Point Location Optimisation via Vehicular Communications)
Periodo di rendicontazione: 2023-01-01 al 2024-12-31
- UNN to SC, 1PM. This secondment was focused on developing a model that incorporates an optimization problem designed to minimize the energy cost of the Ada Byron building at UMA, which encompasses PV panels, a storage system, HVAC, and an electric vehicle charging point. To do so, a price-based control method was considered for the HVAC system. The objective function of the optimization problem encompasses three key terms: the minimization of purchased power from the upstream grid, the degradation cost of the battery, and a penalty term associated with the HVAC system.
- UOU to TAT, 5.9 PM. The secondment focused on two main objectives for public charging places: generating a three-year synthetic EV dataset for training an uncertainty-aware DL model, addressing insufficient real EV data; and designing a charging scheduling algorithm for capacity planning. ML-based models utilized the synthetic dataset, considering factors like EV growth, weather, holidays. The algorithm, including a mixed integer linear problem (MILP) and heuristic online scheduling, aimed at optimizing power demand based on predicted EV arrivals, ensuring a sustainable and efficient public charging infrastructure.
- UMA to TRU, 3.2 PM. The secondment focused on the realization of data predicting layer orchestrating distributed neural networks and implementing incremental training within the Kafka-ML framework. Success in deploying these methods enabled incremental federated training for enhanced data sharing among EVs, improving prediction and scheduling of charging times. The result of this secondment will be sent to an international congress and scientific journals.
- One key result aimed to design efficient algorithms for multi-tier, multi-RAT networks. Active contributions at Princeton led to a technology recognition-based spectrum sensing system, identifying waveforms from diverse networks. The proposed DSS scheme does not require a coordination signaling channel between the LTE and NR networks. Instead, a technology recognition-spectrum sensing system is used to estimate traffic patterns and make spectrum allocation decisions accordingly. The goal is to enhance the seamless integration of these technologies, ensuring reliable communication and connectivity for vehicles in dynamic and varied network environments.
- To train the ML-based uncertainty-aware models, we need a certain amount of real collected EV data. Addressed the challenge of insufficient EV-related Data, particularly for capturing yearly seasonal patterns affected by holidays, events, and the growth of EVs on EV’s arrival date. To tackle this, the synthetic data is generated spanning three years (2020 to 2022) based on one year of collected ACN data. The factors influencing EV arrivals, including EV growth rate, hourly weather conditions, holidays, and events are also incorporated. This generated realistic dataset is utilized to train the uncertainty-aware prediction model.
- This result aimed at defining a general model for a digital twin ecosystem capable of representing standard EVs scenarios. To this end, a detailed investigation was carried out on the various entities that could make up this scenario, such as EVs, charging stations, and connectors, subsequently identifying specific variants and brands (e.g. BEVs, PHEVs, and EREVs for EVs). In the modeling process, digital twin types were designed, where each type abstractly represents an independent entity or variant. Likewise, the composition of digital twins was considered, allowing the decomposition of complex twins into simpler entities through parent-child relationships.