In this work, an enhanced model of injection probes for bulk current injection (BCI) is proposed, allowing for circuit simulation of BCI test setups involving complex wiring harnesses. Accuracy of the proposed model is assessed by means of electromagnetic (EM) full-wave simulation of different wiring structures comprising a large number of conductors. To this end, a virtual calibration setup, including an EM model of the injection device mounted on the calibration jig provided by the probe manufacturer, is preliminary developed and validated versus measurement data. The model is then used to investigate the main factors influencing RF noise injection in practical BCI setups. Particularly, it is used to predict the significantly different levels of noise measured at the terminations of each wire in the bundle during the BCI test, as well as to provide such differences with an explanation in terms of characteristics of the wiring harness, positioning of each conductor in the bundle with respect to the others, equivalent common mode impedance of the terminal networks.

The growing interest in Electrical Vehicles (EVs) opens new possibilities in the use of Li-ion batteries in order to provide ancillary grid services while they are plugged to recharging stations. Indeed, Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B), Vehicle-to-Home (V2H) as well as Vehicle-to-Vehicle (V2V) services can be carried out depending on the particular installation and on the connection to the distribution grid of the considered recharging station. Even if these are interesting and challenging opportunities, the additional charging/discharging cycles necessary to provide these services could decrease the expected life of EV batteries. For this reason, it is of paramount importance to study and develop reliable models of the batteries, which take the aging phenomena affecting the reliability of the Li-ion cells into account to evaluate the best charging/discharging strategy and the economic revenues. To this aim, this paper focuses on a battery pack made up with Li-ion nickel–manganese–cobalt (NMC) cells and proposes a semiempirical Electrothermal Aging Model, which accounts for both calendar and cycle aging. This modeling phase is supported by several experimental data recorded for many charge and discharge cycles at different C-rates and for several temperatures. Thus, it is possible to analyze and compare scenarios considering V2G services or not. Results show that the considered battery is subjected to a life reduction of about 2 years, which is a consequence of the increased Ah charge throughput, which moves from 120,000 Ah over 10 years (scenario without V2G services) to almost 230,000 Ah over 8 years (scenario with V2G services).

Modeling the effects of uncertainty is of crucial importance in the signal integrity and Electromagnetic Compatibility assessment of electronic products. In this article, a novel machine-learning-based approach for uncertainty quantification problems involving both random and epistemic variables is presented. The proposed methodology leverages evidence theory to represent probabilistic and epistemic uncertainties in a common framework. Then, Bayesian optimization is used to efficiently propagate this hybrid uncertainty on the performance of the system under study. Two suitable application examples validate the accuracy and efficiency of the proposed method.

This paper investigates the injection of RF energy onto wiring harness using a tubular wave coupler (TWC). In particular, a transmission line (TL) model of a commercial TWC mounted in the calibration fixture provided by the manufacturer is developed. The proposed model of the calibration setup provides accurate prediction of the measured insertion loss up to 2 GHz. Measurement data and outcomes of three-dimensional electromagnetic simulation of the TWC are used to obtain estimates of some of the involved model parameters, whose direct evaluation is not possible due to partial knowledge of the geometrical/electrical characteristics on the one side, and nonuniform distribution of current and voltage on the TWC cross section on the other. The proposed TL model is used to explain the fundamental phenomena responsible for RF-energy transfer through the TWC. Particularly, it is shown that, differently from bulk current injection probes where inductive coupling is the dominant phenomenon, injection through the TWC involves both inductive and capacitive coupling. This determines a certain amount of directivity of the injection device, and is expected to play a role in RF injection onto complex cable bundles.

Possibility theory is briefly presented and proposed for the evaluation of epistemic uncertainty in electromagnetic-compatibility models. In particular, fuzzy sets are exploited to describe some unknown/uncontrolled model parameters which cannot be rigorously treated through probability theory, since their statistical distribution is unknown. A hybrid approach is used to combine such possibilistic parameters with classical probabilistic parameters described by random variables, so to obtain a general, exhaustive description of uncertainty in model outputs. As an illustrative example, the proposed approach is applied to the estimation of differential mode currents induced in the terminations of a twisted wire pair with an unknown number of twists, illuminated by a random plane-wave field. The obtained results are compared versus conventional Monte Carlo simulations.

In this work, a two-step procedure to predict maximum (worst-case scenario) and minimum (best-case scenario) noise levels induced by bulk current injection (BCI) at the terminal sections of a wiring harness is presented. To this end, common mode (CM) and differential mode (DM) quantities are introduced by a suitable modal transformation, and equivalent modal circuits are derived, where CM (dominant mode) into DM (secondary mode) conversion is modelled by induced sources included into the DM circuit. The procedure initially foresees the solution of the CM circuit to provide input data for subsequent solution of the DM circuit. Such a two-step approach is then used to develop a probabilistic–possibilistic framework for computationally-efficient estimation of lower and upper boundaries to the variability of the noise voltages induced at the bundle terminations. To this end, random uncertainty affecting certain setup parameters is addressed through probability theory, whereas epistemic uncertainty is represented via possibility theory. Accuracy and computational efficiency of the proposed two-step method are assessed by examples involving seven and nineteen wire harnesses.