ROBI’: A prototype mobile manipulator for agricultural applications

The design of ROBI', a prototype mobile manipulator for agricultural applications devised following low-cost, low-weight, simplicity, flexibility and modularity requirements, is presented in this work. The mechanical design and the selection of the main components of the motion control system, including sensors and in-wheel motors, is described. The kinematic and dynamic models of the robot are also derived, with the aim to support the design of a trajectory tracking system and to make a preliminary assessment of the design choices, as well. Finally, two simulations, one~specifically related to a realistic trajectory in an agricultural field, show the validity of these choices

Piece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integration

This paper aims at presenting a novel effective approach to probabilistic analysis of distribution power grid with high penetration of PV sources. The novel method adopts a Gaussian Mixture Model for reproducing the uncertainty of correlated PV sources along with a piece-wise-linear approximation of the voltage-power relationship established by load flow problem. The method allows the handling of scenarios with a large number of uncertain PV sources in an efficient yet accurate way. A distinctive feature of the proposed probabilistic analysis is that of directly providing, in closed-form, the joint probability distribution of the set of observable variables of interest. From such a comprehensive statistical representation, remarkable information about grid uncertainty can be deduced. This includes the probability of violating the safe operation conditions as a function of PV penetration

Modelling of Photovoltaic Systems for Real-Time Hardware Simulation

The real-time simulation is a valid help to test electrical systems when a physical device is not available. This is significantly evident when used in hardware and software co-simulation environment, where it is possible to connect the emulator to a real subsystem to test or validate it. In this paper, a model of the photovoltaic system is presented that can be implemented within a hardware simulator to be able to interface it with a real circuit, the hardware simulator used is the National Instruments RIO system

Modelling and Simulation of Quasi-Resonant Inverter for Induction Heating under Variable Load

Single-switch quasi-resonant DC inverters are preferred in low-power induction-heating applications for their cheapness. However, they pose difficulties in enforcing soft-switching and show limited controllability. A good design of these converters must proceed in parallel with the characterization of the load and the operating conditions. The control of the switching frequency has a critical relationship to the non-linear behavior of the load due to electro-thermal coupling and geometrical anisotropies. Finite element methods enable the analysis of this kind of multiphysics coupled systems, but the simulation of transient dynamics is computationally expensive. The goal of this article is to propose a time-domain simulation strategy to analyze the behavior of induction heating systems with a quasi-resonant single-ended DC inverter using pulse frequency modulation and variable load. The load behavior is estimated through frequency stationary analysis and integrated into the time-domain simulations as a non-linear equivalent impedance parametrized by look-up tables. The model considers variations in temperature dynamics, the presence of work-piece anisotropies, and current harmonic waveforms. The power regulation strategy based on the control of the switch turn-on time is tested in a case study with varying load and it is shown that it is able to maintain the converter in the safe operation region, handling variations up to of (Formula presented.) in the equivalent load resistance

An ElectroThermal Digital Twin for Design and Management of Radiation Heating in Industrial Processes

The design and management of thermoforming systems based on radiation heat transfer require the development of a mathematical model that can be used at all stages of the system's life cycle. For this reason, in this paper, we present a digital twin based on a hybrid ElectroThermal model that can integrate mathematical equations and data acquired in the field. The model's validity is verified with experiments performed on a test bench. The presented model is modular and can be easily used to represent new configurations of the heating elements for simulation and design. Thanks to the low computational complexity of the proposed Digital Twin, it enables the development of advanced control strategies and the analysis and optimization of the main geometric parameters of the system. In addition, it can support the identification of the best configuration and choice of measurement points

Field oriented control dataset of a 3-phase permanent magnet synchronous motor

This paper presents a dataset of a 3-phase Permanent Magnet Synchronous Motor (PMSM) controlled by a Field Oriented Control (FOC) scheme. The data set was generated from a simulated FOC motor control environment developed in Simulink; the model is available in the public GitHub repository1. The dataset includes the motor response to various input signal shapes that are fed to the control scheme to verify the control capabilities when the motor is subjected to real life scenarios and corner conditions. Motor control is one of the most widespread fields in control engineering as it is widely used in machine tools and robots, the FOC scheme is one of the most used control approaches thanks to its performance in speed and torque control, with the drawback of having to handcraft the Proportional-Integrative-Derivative (PID) regulators using Look Up Tables (LUT). The test conditions are designed by setting a motor desired speed. Different input speed variations shapes are proposed as well as extreme scenarios where the linear behaviour of the PID regulator is challenged by applying fast and high magnitude speed variations so that the PID controller is not able to correctly follow the reference. The measured data includes both the outer and inner-loop signals of the FOC, which opens the possibility to develop non-linear control approaches such as Machine Learning (ML) and Neural Networks (NN) with different topologies to replace the linear controllers in the FOC scheme

Towards a comprehensive framework for V2G optimal operation in presence of uncertainty

As the global fleet of Electric Vehicles keeps increasing in number, the Vehicle To Grid (V2G) paradigm is gaining more and more attention. From the grid point of view an aggregate of electric vehicles can act as a flexible load, thus able to provide balancing services. The problem of computing the optimal day-ahead charging schedule for all vehicles in the fleet is a challenging one, especially because it is affected by many sources of uncertainty. In this paper we consider the uncertainty deriving from arrival and departure times, arrival energy and services market outcomes. We propose a general optimization framework to deal with the day ahead planning that encompasses different kind of use-cases. We adopt a robust paradigm to enforce the constraints and an expectation paradigm for the cost function. For all constraints and cost terms we propose an exact formulation or a very tight approximation, even in the case of piece-wise linear battery dynamics. Numerical results corroborates the theoretical findings

Electric Vehicles Charging Sessions Classification Technique for Optimized Battery Charge Based on Machine Learning

The fast increase in electric vehicle (EV) usage in the last 10 years has raised the need to properly forecast their energy consumption during charge. Lithium-ion batteries have become the major storage component for electric vehicles, avoiding their overcharge can preserve their health and prolong their lifetime. This paper proposes a Machine Learning model based on the K-Nearest Neighbors classification algorithm for EV charging session duration forecast. The model forecasts the duration of the charge by assigning the event to its correct class. Each class contains the charging events whose duration is comprised of a certain interval. The only information used by the algorithm is the one available at the beginning of the charging event (arrival time, starting SOC, calendar data). The model is validated on a real-world dataset containing records of charging sessions from more than 100 users, a sensitivity analysis is performed to assess the impact of different information given as input. The effectiveness of the model with respect to the benchmark models is demonstrated with an increase in performance

A virtual sensor for electric vehicles’ state of charge estimation

The estimation of the state of charge is a critical function in the operation of electric vehicles. The battery management system must provide accurate information about the battery state, even in the presence of failures in the vehicle sensors. This article presents a new methodology for the state of charge estimation (SOC) in electric vehicles without the use of a battery current sensor, relying on a virtual sensor, based on other available vehicle measurements, such as speed, battery voltage and acceleration pedal position. The estimator was derived from experimental data, employing support vector regression (SVR), principal component analysis (PCA) and a dual polarization (DP) battery model (BM). It is shown that the obtained model is able to predict the state of charge of the battery with acceptable precision in the case of a failure of the current sensor