Mario Silvagni

Purpose: This paper aims to identify the characteristics of two AXI 4130 brushless direct current (BLDC) motors and the propeller-motor performance of five Aeronaut CAM folding propellers. The resulting data sets can be used to select the optimal propeller for small Unmanned Aerial Vehicles. Design/methodology/approach: The characteristics of AXI 4130–16 and AXI 4130–20 motors were identified under various supply voltages, as these motors support several battery-cell configurations. Following the motor characterization, five different propellers were tested on a LY-Micro-Max UAV Power System test bench using the selected AXI 4130–20 motor to estimate its thrust, torque, input power and revolutions per minute under static conditions. Findings: The obtained data sets provide a reliable basis for optimizing propeller-motor combinations, predicting Unmanned Aerial Vehicle performance under static conditions and selecting the most efficient propeller. The presented analysis confirmed that the 18x11-inch propeller delivers the highest propulsion force and the best thrust-to-torque ratio. If this is used as the primary criterion, the selection of the optimal propeller for a small Unmanned Aerial Vehicle can be easily determined. Practical implications: The data presented in this paper is novel and specifically tailored for small Unmanned Aerial Vehicle design. Originality/value: This paper provides specific characteristics of BLDC motors that are often difficult to find in existing literature. The identified propeller characteristics can be used to select the optimal propeller for small Unmanned Aerial Vehicles.

Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium-ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium-ion batteries. The ANN-based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large-scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real-time execution speed of 8.34 µs is possible with a negligible memory occupation.

The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 μs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications.

This paper presents a multipurpose UAV (unmanned aerial vehicle) for mountain rescue operations. The multi-rotors based flying platform and its embedded avionics are designed to meet environmental requirements for mountainous terrain such as low temperatures, high altitude and strong winds, assuring the capability of carrying different payloads (separately or together) such as: avalanche beacon (ARTVA) with automatic signal recognition and path following algorithms for the rapid location of snow-covered body; camera (visible and thermal) for search and rescue of missing persons on snow and in woods during the day or night; payload deployment to drop emergency kits or specific explosive cartridge for controlled avalanche detachment. The resulting small (less than 5 kg) UAV is capable of full autonomous flight (including take-off and landing) of a pre-programmed, or easily configurable, custom mission. Furthermore, the autopilot manages the sensors measurements (i.e. beacons or cameras) to update the flying mission automatically in flight. Specific functionalities such as terrain following were developed and implemented. Ground station programming of the UAV is not needed, except compulsory monitoring, as the rescue mission can be accomplished in a full automatic mode.

The vibration control of rotors is often performed using elastomeric or fluid dampers together with rolling element or hydrodynamic type bearings. Electromagnetic dampers seem a valid alternative to conventional solutions and also to active magnetic bearings (AMBs) because their simpler architecture, size and, if of transformer type, also for the absence of power electronics, position sensors, and any fast feedback loop. However, transformer eddy current dampers require a constant voltage power supply than can be provided by an embedded generator to reduce cost and improve the reliability. The present paper proposes a self-powered damper to fulfill these requirements. A three-phase permanent magnet electric generator (connected to the rotating shaft) generates the required power for the damping device. The generator is connected to the damping circuit by means of tuned impedance and a three-phase rectifier.