Separating multiscale Battery dynamics and predicting multi-step ahead voltage simultaneously through a data-driven approach

Accurate prediction of battery performance under various ageing conditions is necessary for reliable and stable battery operations. Due to complex battery degradation mechanisms, estimating the accurate ageing level and ageing-dependent battery dynamics is difficult. This work presents a health-aware battery model that is capable of separating fast dynamics from slowly varying states of degradation and state of charge (SOC). The method is based on a sequence-to-sequence learning-based encoder-decoder model, where the encoder infers the slowly varying states as the latent space variables in an unsupervised way, and the decoder provides health-aware multi-step ahead prediction conditioned on slowly varying states from the encoder. The proposed approach is verified on a Lithium-ion battery ageing dataset based on real driving profiles of electric vehicles.Comment: 6 pages, 10 figures, IEEE Vehicle Power and Propulsion confernce(IEEE VPPC 2023

Hybrid Design of Multiplicative Watermarking for Defense Against Malicious Parameter Identification

Watermarking is a promising active diagnosis technique for detection of highly sophisticated attacks, but is vulnerable to malicious agents that use eavesdropped data to identify and then remove or replicate the watermark. In this work, we propose a hybrid multiplicative watermarking (HMWM) scheme, where the watermark parameters are periodically updated, following the dynamics of the unobservable states of specifically designed piecewise affine (PWA) hybrid systems. We provide a theoretical analysis of the effects of this scheme on the closed-loop performance, and prove that stability properties are preserved. Additionally, we show that the proposed approach makes it difficult for an eavesdropper to reconstruct the watermarking parameters, both in terms of the associated computational complexity and from a systems theoretic perspective.Comment: 8 pages, first submission to the 62nd IEEE Conference on Decision and Contro

Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.Comment: 7 pages, 2 figures. Accepted at the 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - SAFEPROCESS 202

Design of multiplicative watermarking against covert attacks

This paper addresses the design of an active cyberattack detection architecture based on multiplicative watermarking, allowing for detection of covert attacks. We propose an optimal design problem, relying on the so-called output-to-output l2-gain, which characterizes the maximum gain between the residual output of a detection scheme and some performance output. Although optimal, this control problem is non-convex. Hence, we propose an algorithm to design the watermarking filters by solving the problem suboptimally via LMIs. We show that, against covert attacks, the output-to-output l2-gain is unbounded without watermarking, and we provide a sufficient condition for boundedness in the presence of watermarks.Comment: 6 page conference paper accepted to the 60th IEEE Conference on Decision and Contro

Plasma exchange in acute and chronic hyperviscosity syndrome: a rheological approach and guidelines study

Therapeutic plasma exchange is an extra-corporeal technique able to remove from blood macromolecules and/or replace deficient plasma factors. It is the treatment of choice in hyperviscosity syndrome, due to the presence of quantitatively or qualitatively abnormal plasma proteins such as paraproteins. In spite of a general consensus on the indications to therapeutic plasma exchange in hyperviscosity syndrome, data or guide lines about the criteria to plan the treatment are still lacking. We studied the rheological effect of plasma exchange in 20 patients with plasma hyperviscosity aiming to give data useful for a rational planning of the treatment. Moreover, we verified the clinical applicability of the estimation of plasma viscosity by means of Kawai's equation. Plasma exchange decreases plasma viscosity about 20-30% for session. Only one session is required to normalize plasma viscosity when it is 2.2 till to 6 mPas. A fourth session is useless, especially if the inter-session interval is < 15 days. By means of a polynomial equation, knowing basal-plasma viscosity and the disease of a patient, we can calculate the decrease of viscosity obtainable by each session of plasma exchange then the number of session required to normalize the viscosity. Kawai's equation is able to evaluate plasma viscosity in healthy volunteers, but it is not clinically reliable in paraproteinemias. [Pubmed] [Scholar] [EndNote] [BibTex

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