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Abstract The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the Rds(on), in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the Rds(on)) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the Rds(on), based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

Abstract In this paper, a resonant converter model is proposed that takes into account the thermal and electrical dynamics of the main components. With respect to state‐of‐art, where model‐based design approaches are often implemented off‐line, the proposed model has a low computational complexity such that it can be simulated in real‐time. This real‐time electro‐thermal model enables also the design of a predictive control algorithm, that is proposed to optimize the current control of a SiC‐based resonant converter by handling both thermal and electrical operating limits of its components. To validate the interaction between the converter model and the predictive control algorithm, validation is proposed via real‐time processor‐in‐the‐loop, with application to the on‐board charger for high‐voltage Li‐ion battery pack, typical of modern fully electrified vehicles.

Self-propelled wheelchairs are challenging to drive on off-road routes or require enormous physical effort in situations where the gradient exceeds 8%. For most people, these situations are too strenuous and therefore impractical. This work presents an innovative plug-and-play system to electrify a manual wheelchair. With the constraint of not irreversibly modifying the original wheelchair, a mechanical system is developed to allow motors to turn the rear wheels by friction. Torque and power requirements have been derived by modeling and simulating in Matlab the physical system. To manufacture a working prototype, a control system using standard components is devised, and object-oriented firmware is developed with the C++ programming language. The result is a plug-and-play portable kit that can be adapted to wheelchairs of any size, and the expandable Human-Machine-Interface (HMI) makes it ready to expand the audience that can use it.

The paper presents a precise and efficient model of Double-Side Cooled (DSC) SiC MOSFET, which incorporates the dynamics of both electrical and thermal variables. It offers a suitable computational complexity for simulating transients in complex power converters. The objective is to define a model that enables multi-scale time simulations and facilitates rapid power converter design in system-level tools such as Simulink. Additionally, the model aims to achieve simulation accuracy comparable to device-level models for the next generation of SiC MOSFETs. The paper demonstrates the complete test bench measurement procedure for the device. This procedure is essential for experimentally extrapolating the intrinsic characteristics and developing a model-reduction approach based on electro-thermal modeling. The approach strikes a balance between computational complexity and level of detail. The proposed model has been seamlessly integrated into Simulink to simulate a 3-phase inverter for several grid cycles at the grid frequency. To evaluate the model’s validity, the predicted inverter performance is compared with experimental measurements. These simulations require significantly less time compared to those based on LTspice models.

This research introduces a model-based approach for the analysis and control of an onboard charger (OBC) system for contemporary electrified vehicles. The primary objective is to integrate the modeling of SiC/GaN MOSFETs electrothermal behaviors into a unified simulation framework. The motivation behind this project stems from the fact that existing literature often relies on finite element method (FEM) software to examine thermal dynamics, necessitating the development of complex models through partial derivative equations. Such intricate models are computationally demanding, making it difficult to integrate them with circuit equations in the same virtual environment. As a result, lengthy wait periods and a lack of communication between the electrothermal models limit the thorough study that can be conducted during the design stage. The selected case study for examination is a modular 1ϕ (single phase) onboard computer (OBC). This system comprises a dual active bridge (DAB) type DC/DC converter, which is positioned after a totem pole power factor correction (PFC) AC/DC converter. Specifically, the focus is directed toward a 7 kW onboard computer (OBC) utilizing high-voltage SiC/GaN MOSFETs to ensure optimal efficiency and performance. A systematic approach is presented for the assessment and selection of electronic components, employing circuit models for the totem pole power factor correction (PFC) and dual active bridge (DAB) converter. These models are employed in simulations closely mimicking real-world scenarios. Furthermore, rigorous testing of the generated models is conducted across a spectrum of real-world operating conditions to validate the stability of the implemented control algorithms. The validation process is bolstered by a comprehensive exploration of parametric variations relative to the nominal case. Notably, each simulation adheres to the recommended operational limits of the selected components and devices. Detailed data sheets encompassing electrothermal properties are provided for contextual reference.

Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation.

In recent decades, an exponential surge in technological advancements has significantly transformed various aspects of daily life. The proliferation of indispensable objects such as smartphones and computers underscores the pervasive influence of technology. This trend extends to the domains of the healthcare, automotive, and industrial sectors, with the emergence of remote-operating capabilities and self-learning models. Notably, the automotive industry has integrated numerous remote access points like Wi-Fi, USB, Bluetooth, 4G/5G, and OBD-II interfaces into vehicles, amplifying the exposure of the Controller Area Network (CAN) bus to external threats. With a recognition of the susceptibility of the CAN bus to external attacks, there is an urgent need to develop robust security systems that are capable of detecting potential intrusions and malfunctions. This study aims to leverage fingerprinting techniques and neural networks on cost-effective embedded systems to construct an anomaly detection system for identifying abnormal behavior in the CAN bus. The research is structured into three parts, encompassing the application of fingerprinting techniques for data acquisition and neural network training, the design of an anomaly detection algorithm based on neural network results, and the simulation of typical CAN attack scenarios. Additionally, a thermal test was conducted to evaluate the algorithm’s resilience under varying temperatures.

This work exploits the concept of one-class classifier applied to the problem of anomaly detection in communication networks. The article presents the design of an innovative anomaly detection algorithm based on polynomial interpolation technique and statistical analysis. The innovative method is applied to datasets largely used in the scientific community for bench-marking such as KDD99, UNSW-NB15 and CSE-CIC-IDS-2018, and further evaluated with application to a novel available dataset EDGE-IIOTSET 2022. The paper also reports experimental results showing that the proposed methodology outperforms classic one-class classifiers, such as Extreme Learning Machine and Support Vector Machine models, and rule-based intrusion detection system like SNORT. With respect to binary classifiers, this work has the advantage of not requiring any a-priori knowledge about attacks and is based on the collection of only normal data traffic.

For safety-critical applications, the validation process using a model-based approach plays an increasingly important role. In this paper we propose the application of a predictive control algorithm, entirely implemented in low-level code, to the use case of assisted driving of four-wheel vehicles. The aim is to present the workflow for the validation of an advanced control algorithm and its implementation on an Embedded system, representative of the computational capabilities of Automotive ECUs. The proposed validation exploits the SIL (Software-In-the-Loop) and PIL (Processor-In-the-Loop) paradigms to analyse the combination of control parameters and factors related to the choice of the mathematical model describing the vehicle behaviour and the choice of the numerical algorithms selected to approximate the differential equations.

The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary to enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. This research aims to explore machine learning (ML) approaches for IDS, specifically focusing on datasets, machine algorithms, and metrics. Three datasets were utilized in this study: KDD 99, UNSW-NB15, and CSE-CIC-IDS 2018. Various machine learning algorithms were chosen and examined to assess IDS performance. The primary objective was to provide a taxonomy for interconnected intrusion detection systems and supervised machine learning algorithms. The selection of datasets is crucial to ensure the suitability of the model construction for IDS usage. The evaluation was conducted for both binary and multi-class classification to ensure the consistency of the selected ML algorithms for the given dataset. The experimental results demonstrated accuracy rates of 100% for binary classification and 99.4In conclusion, it can be stated that supervised machine learning algorithms exhibit high and promising classification performance based on the study of three popular datasets.

This review of the state of the art aims to collect the description and main research results in the field of development and validation of control algorithms with the main purpose to solve the problem of cogging torque and main sources of electromagnetic torque ripple. In particular, we focus on electric drives for advanced and modern mechatronic applications such as industrial automation, robotics, and automotive applications, with special emphasis on work that exploits model-based design. A great added value of this paper is to explicitly show the operational steps required for the model-based design design of optimized control algorithms for electric drives where it is necessary to make up for electromagnetic torque oscillations due to the main sources of ripple, particularly cogging torque. The ultimate goal of this paper is to provide researchers approaching this particular problem with a comprehensive collection of the most effective solutions reported in the state of the art and also a summary for effectively applying the model-based design methodology.

The use of machine-learning techniques is becoming more and more frequent in solving all those problems where it is difficult to rationally interpret the process of interest. Intrusion detection in networked systems is a problem in which, although it is not fundamental to interpret the measures that one is able to obtain from a process, it is important to obtain an answer from a classification algorithm if the network traffic is characterized by anomalies (and hence, there is a high probability of an intrusion) or not. Due to the increased adoption of SW-defined autonomous systems that are distributed and interconnected, the probability of a cyber attack is increased, as well as its consequence in terms of system reliability, availability, and even safety. In this work, we present the application of different machine-learning models to the problem of anomaly classification in the context of local area network (LAN) traffic analysis. In particular, we present the application of a K-nearest neighbors (KNN) and of an artificial neural network (ANN) to realize an algorithm for intrusion detection systems (IDS). The dataset used in this work is representative of the communication traffic in common LAN networks in military application in particular typical US Air Force LAN. This work presents a training phase of the different models based on a multidimensional-scaling preprocessing procedure, based on different metrics, to provide higher performance and generalization with respect to model prediction capability. The obtained results of KNN and ANN classifiers are compared with respect to a commonly used index of performance for classifiers evaluation.

Mechatronic systems are a class of cyber-physical systems, whose increasing complexity makes their validation and verification more and more difficult, while their requirements become more challenging. This paper introduces a development method based on model-based design, co-simulation and formal verification. The objective of this paper is to show the applicability of the method in an industrial setting. An application case study comes from the field of precision servo-motors, where formal verification has been used to find acceptable intervals of values for design parameters of the motor controller, which have been further explored using co-simulation to find optimal values. The reported results show that the method has been applied successfully to the case study, augmenting the current model-driven development processes by formal verification of stability, formal identification of acceptable parameter ranges, and automatic design-space exploration.

This work presents an innovative control architecture, which takes its ideas from the theory of adaptive control techniques and the theory of statistical learning at the same time. Taking inspiration from the architecture of a classical neural network with several hidden levels, the principle is to divide the architecture of the adaptive controller into three different levels. Each level implements an algorithm based on learning from data and therefore we can talk about learning concepts. Each level has a different task: the first to learn the required reference to the control loop; the second to learn the coefficients of the state representation of a model of the system to be controlled; and finally, the third to learn the coefficients of the state representation of the actual controller. The design of the control system is reported from both a rigorous and an operational point of view. As an application example, the proposed control technique is applied on a second-order non-linear system. We consider a servo-drive based on a brushless DC (BLDC) motor, whose dynamic model considers all the non-linear effects related to the electromechanical nature of the electric machine itself, and also an accurate model of the switching power converter. The reported example shows the capability of the control algorithm to ensure trajectory tracking while allowing for disturbance rejection with different disturbance signal amplitude. The implementation complexity analysis of the new controller is also proposed, showing its low overhead vs. basic control solutions.

This work addresses the problem of mitigating the effects of the cogging torque in permanent magnet synchronous motors, particularly brushless motors, which is a main issue in precision electric drive applications. In this work, a method for mitigating the effects of the cogging torque is proposed, based on the use of a nonlinear automatic control technique known as feedback linearization that is ideal for underactuated dynamic systems. The aim of this work is to present an alternative to classic solutions based on the physical modification of the electrical machine to try to suppress the natural interaction between the permanent magnets and the teeth of the stator slots. Such modifications of electric machines are often expensive because they require customized procedures, while the proposed method does not require any modification of the electric drive. With respect to other algorithmic-based solutions for cogging torque reduction, the proposed control technique is scalable to different motor parameters, deterministic, and robust, and hence easy to use and verify for safety-critical applications. As an application case example, the work reports the reduction of the oscillations for the angular position control of a permanent magnet synchronous motor vs. classic PI (proportional-integrative) cascaded control. Moreover, the proposed algorithm is suitable to be implemented in low-cost embedded control units.