The OPtimization of Electric Vehicle Autonomy (OPEVA) project enhances data aggregation for Electric Vehicles (EVs) by collecting critical real-time data (i.e., vehicle performance, battery health, charging behaviours) through heterogeneous data acquisition devices built on robust HW and integrated with Internet of Things (IoT) protocols. By combining internal sensor data and driver-specific behaviours with external information (e.g., road conditions, charging station availability), OPEVA maximizes vehicles performance, establishing secure and seamless data communication between EVs and the infrastructure, and using IoT and cloud computing tools alongside Vehicle-to-Everything (V2X) devices and networks. This paper focuses on the extensible data model ensuring semantic data integrity considering in- and out-vehicle factors, presenting data acquisition solutions dealing with OPEVA's semantic data model and their use in various Artificial Intelligence (AI)-powered use cases (e.g., range prediction, route optimization, battery management).

This article describes sensors and radio frequency (RF) components manufactured by applying selective-metallization processes (metal foil tape, liquid metal filling, electro and electroless plating, and surface embossing) to 3-D-printed and flexible dielectric substrates. All these technologies can be implemented at room temperature, thus enabling the adoption of unconventional materials and biopolymers with low glass transition temperatures. In this article, we also describe how the above technologies are used to manufacture wireless transponders for tracking and sensing applications. Several examples of RF components are reported, including antennas, beamforming networks, Doppler radars, and wireless passive transponders based on backscatter radio. Innovative circuit design approaches (such as the via-less approach) are presented and their impact on circuit manufacturing and recyclability is discussed.

River discharge is an important variable to measure in order to predict droughts and flood occurrences. Once the cross-sectional geometry of the river is known, discharge can be inferred from water level and surface flow velocity measurements. Since river discharges are of particular interest during extreme weather events, when river sites cannot be safely accessed, noncontact sensing technologies are particularly appealing. To this purpose, this work proposes a prototype of a low-cost continuous wave (CW) Doppler radar sensor, which is able to monitor the surface flow velocity of rivers. The prototype is tested at two gauged sites in central Italy, along the Tiber River. The surface flow velocity distribution across the river is monitored by means of the analysis of received Doppler signal. The surface velocity statistics are then extracted using a novel algorithm that is optimized to run on a microprocessor platform with minimal computing power (ArduinoUNO). In particular, the radar measurements are used to initialize a 2-D entropy-based velocity model (EVM) that is able to estimate river discharges in any flow condition. Finally, the results concerning the observed discharge provided by the EVM prove to be comparable with those obtained with more expensive commercial solutions. The results are important since the described methodology can be extended to small-size Doppler radar sensors onboard unmanned aerial vehicles (UAVs), the latter providing a method for mapping surface velocity of rivers.