Modeling the effects of uncertainty is of crucial importance in the Signal Integrity (SI) and Electromagnetic Compatibility (EMC) assessment of electronic products. In this paper, 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.
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 probabilisticpossibilistic 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.
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).
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