Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio

Topological analysis of powertrains for refusecollecting vehicles based on real routes – Part II: Hybrid electric powertrain

In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this second part, three different hybrid electric powertrain architectures are proposed and modeled. These architectures are based on the use of fuel cells, ultracapacitors, and batteries. A calculation engine, which is specifically designed to estimate energy consumption, respecting the original performance as the original internal combustion engine (ICE), is presented and used for simulations and component sizing. Finally, the overall performance of the different architectures (hybrid hydraulic, taken from the first paper part, and hybrid electric, estimated in this second part) and control strategies are summarized in a fuel and energy consumption table. Based on this table, an analysis of the different architecture performance results is carried out. From this analysis, a technological evolution of these vehicles in the medium- and long terms is proposed.Postprint (author's final draft

Topological analysis of powertrains for refusecollecting vehicles based on real routes – Part I: Hybrid hydraulic powertrain

In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on the simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this first part, a characterization of a standard route is performed, analyzing the average power consumption and the most frequent working points of an internal combustion engine (ICE) in real routes. This information is used to define alternative powertrain architectures. A hybrid hydraulic powertrain architecture is proposed and modelled. The proposed powertrain model is executed using two different control algorithms, with and without predictive strategies, with data obtained from real routes. A calculation engine (an algorithm which runs the vehicle models on real routes), is presented and used for simulations. This calculation engine has been specifically designed to analyze if the different alternative powertrain delivers the same performance of the original ICE. Finally, the overall performance of the different architectures and control strategies are summarized into a fuel and energy consumption table, which will be used in the second part of this paper to compare with the different architectures based on hybrid electric powertrain. The overall performance of the different architectures indicates that the use of a hybrid hydraulic powertrain with simple control laws can reduce the fuel consumption up to a 14 %.Postprint (author's final draft

Thermal Management in Plug-In Hybrid Electric Vehicles: a Real-Time Nonlinear Model Predictive Control Implementation

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A real-time nonlinear model predictive control (NMPC) for the thermal management (TM) of the electrical components cooling circuit in a Plug-In Hybrid Electric Vehicle (PHEV) is presented. The electrical components are highly temperature-sensitive and therefore working out of the ranges recommended by the manufacturer can lead to their premature aging or even failure. Consequently, the goals for an accurate and efficient TM are two: to keep the main component, the Li-ion battery, within optimal working temperatures, and to consume the minimum possible electrical energy through the cooling circuit actuators. This multi-objective requirement is formulated as a finite-horizon optimal control problem (OCP) that includes a multi-objective cost function, several constraints and a prediction model especially suitable for optimization. The associated NMPC is performed on real-time by the optimization package MUSCOD-II and is validated in three different repeatable test-drives driven with a PHEV. Starting from identical conditions, each cycle is driven once being the cooling circuit controlled with NMPC and once with a conventional approach based on a finite-state machine. Compared to the conventional strategy, the NMPC proposed here results in a more accurate and healthier temperature performance, and at the same time, leads to reductions in the electrical consumption up to 8%.Postprint (author's final draft

FPGA-based implementation of the instantaneus frequency estimation of phonocardiographic signals

The instantaneous frequency can be used to provide information about how the frequency content of the phonocardiogram signal varies in time, in order to characterize the heart sounds and murmurs. The instantaneous frequency of a signal can be calculated from the discrete Hilbert transform, computed through the moving discrete Hartley transform, which reduces the computation time. To compute in real time the instantaneous frequency, the algorithms have been implemented in a FPGA device, exploiting the high performance and flexibility of reconfigurable hardware. The results obtained from the FPGA show high accuracy in comparison to those computed with MatlabThis work has been supported by Fundación Séneca of Región de Murcia and Ministerio de Ciencia y Tecnología of Spain, under grants PB/63/FS/02 and TIC2003-09400-C04-02, respectively

Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

Background: Alzheimer’s disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10 % to 15 % per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. Method: The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of one vs. rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. Results: The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25 % classification score on the test subset which consists of 160 real subjects. Comparison with Existing Method(s): The classifier yielded the best performance when compared to: i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and iii) bagging ensemble learning. Conclusions: A robust method has been proposed for the international challenge on MCI prediction based on MRI data.This work was supported by the MINECO/FEDER under TEC2015-64718-R project, the Consejería de Economía, Innovacion, Ciencia, y Empleo of the Junta de Andalucía under the P11-TIC-7103 Excellence Project and the Salvador de Madariaga Mobility Grants 2017

Laponite Composites:In Situ Films Forming as a Possible Healing Agent

A healing material must have desirable characteristics such as maintaining a physiological environment, protective barrier-forming abilities, exudate absorption, easy handling, and non-toxicity. Laponite is a synthetic clay with properties such as swelling, physical crosslinking, rheological stability, and drug entrapment, making it an interesting alternative for developing new dressings. This study evaluated its performance in lecithin/gelatin composites (LGL) as well as with the addition of maltodextrin/sodium ascorbate mixture (LGL MAS). These materials were applied as nanoparticles, dispersed, and prepared by using the gelatin desolvation method—eventually being turned into films via the solvent-casting method. Both types of composites were also studied as dispersions and films. Dynamic Light Scattering (DLS) and rheological techniques were used to characterize the dispersions, while the films’ mechanical properties and drug release were determined. Laponite in an amount of 8.8 mg developed the optimal composites, reducing the particulate size and avoiding the agglomeration by its physical crosslinker and amphoteric properties. On the films, it enhanced the swelling and provided stability below 50 °C. Moreover, the study of drug release in maltodextrin and sodium ascorbate from LGL MAS was fitted to first-order and Korsmeyer–Peppas models, respectively. The aforementioned systems represent an interesting, innovative, and promising alternative in the field of healing materials.</p

Towards a Comprehensive Asset Integrity Management (AIM) Approach for European Infrastructures

Abstract Transport infrastructure is the backbone of national economies, providing connections for people and goods, access to jobs and services, and enabling trade and economic growth. It is of paramount importance to preserve, maintain and upgrade the infrastructure network so that to sustain the economic growth and an intelligent mobility. Asset Integrity Management (AIM) approaches will therefore represent key tools for facing the infrastructure maintenance issue and for tackling the ageing that characterize already existing assets. This paper, starting from analyzing the current state of the art solutions in assets management (Enevoldsen, I., 2008), proposes a comprehensive AIM approach that aims at replacing current time-based approaches with a performance-based approach that can systematically take into account the dynamic nature of the transport network. This means moving from a deterministic to a probabilistic approach in design, rehabilitation and retrofitting of infrastructures for increasing life-time and reducing maintenance costs. Such approach therefore laid the basis of secure sustainable impact since by improving awareness and reducing uncertainties, it might allow achieving an optimal balance among available resources and planning of investments

Therapeutic Effects of Anti-Bone Morphogenetic Protein and Activin Membrane-Bound Inhibitor Treatment in Psoriasis and Arthritis

Abstract Objective: The transforming growth factor ? (TGF?) inhibitor BAMBI (bone morphogenetic protein and activin membrane-bound inhibitor) has been shown to control differentiation of CD4+ T lymphocytes into either tolerogenic Treg cells or pathogenic Th17 cells, through the regulation of TGF? and interleukin-2 (IL-2) signaling strength. The present study was undertaken to explore the potential beneficial effects of this strategy of pharmacologic inhibition using novel anti-BAMBI monoclonal antibodies (mAb) in different experimental murine models of chronic skin and joint inflammatory/autoimmune disease. Methods: Development of Saccharomyces cerevisiae mannan-induced psoriatic arthritis (MIP) (n = 18-30 mice per group), imiquimod-induced skin psoriasis (n = 20-30 mice per group), or type II collagen-induced arthritis (CIA) (n = 13-16 mice per group) was analyzed in a total of 2-5 different experiments with either wild-type (WT) or BAMBI-deficient B10.RIII mice that were left untreated or treated with mAb B101.37 (mouse IgG1 anti-BAMBI), a mouse IgG1 anti-TNP isotype control, anti-CD25, or anti-TGF? mAb. Results: Treatment of normal mice with IgG1 anti-BAMBI mAb clone B101.37 led to expansion of Treg cells in vivo, and had both preventive and therapeutic effects in mice with MIP (each P < 0.05 versus controls). The conferred protection against disease progression was found to be mediated by Treg cells, which controlled the activation and expansion of pathogenic IL-17-producing cells, and was dependent on the level of TGF? activity. Furthermore, treatment with B101.37 mAb blocked both the development of skin psoriasis induced by imiquimod and the development of CIA in mice (each P < 0.05 versus controls). Finally, pharmacologic inhibition of BAMBI with the IgM anti-BAMBI mAb B143.14 also potentiated the suppressive activity of Treg cells in vitro (P < 0.001 versus controls). Conclusion: These results in murine models identify BAMBI as a promising new therapeutic target for chronic inflammatory diseases and other pathologic conditions modulated by Treg cells.Funding was provided by grants from the Spanish Ministerio de Economía y Competitividad (Plan Nacional I+D+i) co-financed by European Development Regional Fund to RM (SAF2017-82905-R) and JM (SAF2016-75195-R). PA and MI were partially supported by grants from “Luchamos por la Vida Foundation” and the Spanish Ministerio de Economía y Competitividad (IPT2011-1527-010000) associated with Fibrostatin SL, respectivel

Investigation of the fusion process for B 10 + Au 197 at near-barrier energies

In a previous work, we presented data for the B10+Au197 system, corresponding to quasielastic and elastic scattering, Au197 inelastic excitation, and one neutron pickup transfer, measuring the angular distribution of scattered beam-like ejectiles at several energies around the Coulomb barrier. In this paper, we present data for the fusion process of the same system, at several energies around the Coulomb barrier, as well as new data for one neutron pickup and stripping transfer. In this case, we detected offline γ rays stemming from the β-delayed decay chain of fusion-evaporation residues and heavy transfer products. As in our previous work, we analyzed this data set with coupled reaction channels calculations using the São Paulo potential. We show that the coupling to the one neutron transfer channel is quite important to describe the fusion data at the sub-barrier energy region. We also provide a comparison of the experimental fusion cross sections obtained for B10+Au197 with data for several other systems involving the same target nucleus.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina) PIP00786COFondo para la Investigacin Cientfica y Tecnolgica (FONCYT, Argentina) PICT-2017-4088Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, Brazil) 2018/09998-8, 2019/07767-1, 2019/05769-7Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil) 302160/2018-3, 304056/2019-7Ministerio de Ciencia, Innovación y Universidades PGC2018-096994-B-C2