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Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a... more
Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense...
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Parameter estimation of electrical vehicle batteries in the presence of temperature effect is addressed in this work. A simple parametric temperature dependent battery model is used for this purpose where the temperature dependence is... more
Parameter estimation of electrical vehicle batteries in the presence of temperature effect is addressed in this work. A simple parametric temperature dependent battery model is used for this purpose where the temperature dependence is described by static relationships. A two-stepmethod is used that includes a parameter estimation step of the key parameters at differenttemperatures followed by a static optimization step that determines the temperature coefficients of thecorresponding parameters. It was found that the temperature dependent parameter characteristicscan be reliably estimated from charging profiles only. The proposed method can be used as acomputationally effective way of determining the key battery parameters at a given temperature fromtheir actual estimated values and from their previously determined static temperature dependence.The proposed parameter estimation method was verified by simulation experiments on a morecomplex battery model that also describes the detail...
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LQ Control of Lotka-Volterra Systems Based on their Locally Linearized Dynamics**This research is supported by the National Research, Development and Innovation Office - NKFIH through grant No. 115694, ***A. Magyar was supported by the János Bolyai Research Scholarship of the Hungarian Academy of...more
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ABSTRACT —A multiple-input–multiple-output linear-quadratic servo controller is proposed for a synchronous generator operating in a nuclear power plant that keeps the active power at the desired level and performs reactive power reference... more
ABSTRACT —A multiple-input–multiple-output linear-quadratic servo controller is proposed for a synchronous generator operating in a nuclear power plant that keeps the active power at the desired level and performs reactive power reference tracking using the reactive power demand from a central dispatch center. The controller design was based on the locally linearized version of a previous non-linear dynamical model of the synchronous electrical generator [1, 2], the parameters of which have been identified using measured data from Paks Nuclear Power Plant (Hungary). The method can easily be applied to any industrial power plant generator connected to the electrical grid after estimating its parameters. The proposed observer-based multiple-input–multiple-output state feedback controller is a linear-quadratic servo controller with very good reference tracking and disturbance rejection properties, which were confirmed by simulation experiments.
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Various types of control problems related to distributed control system structure design, such as the analysis of structural controllability and observability, analysis of disturbance rejectivity, analysis of structural stability, design... more
Various types of control problems related to distributed control system structure design, such as the analysis of structural controllability and observability, analysis of disturbance rejectivity, analysis of structural stability, design of distributed SISO (single input single output) controller system structures, and the design of distributed MIMO (multiple input multiple output) controller system structures, are described in the paper as algorithmic problems. Their equivalent graph theoretic problems and their algorithmic properties are also analysed.
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A dinamikus folyamatrendszerek modellezése és irányítása területén folytattunk kutatást a folyamatmérnöki, valamint a rendszer- és irányításelméleti módszereket integrálva. 1. Számítógéppel segített folyamatmodellezés A folyamatmodellek... more
A dinamikus folyamatrendszerek modellezése és irányítása területén folytattunk kutatást a folyamatmérnöki, valamint a rendszer- és irányításelméleti módszereket integrálva. 1. Számítógéppel segített folyamatmodellezés A folyamatmodellek osztályán bevezettük a minimális modell fogalmát, és eljárásokat javasoltunk minimális modellek felállítására modellredukcióval, illetve inkrementális modellépítéssel. A komplex folyamatrendszerek dinamikus modellezésére használatos többléptékű (multi-scale) modellekre kidolgoztunk egy diagnosztikai cél-vezérelt modellezési módszert és egy ezen alapuló intelligens diagnosztikai rendszert. Kidolgoztuk a többléptékű modellek skálatérképen alapuló egyszerűsítésének módszerét. 2. Nemlineáris folyamatrendszerek analízise és irányítása A kvázipolinom modellekkel leírható folyamatrendszerek esetén lineáris mátrix egyenlőtlenségekkel leírható feltételeket adtunk a stabilitásra, és kvadratikus hamiltoni struktúrák létezésére. Módszert adtunk statikus kvázipol...
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ABSTRACT An indirect measurement scheme is used in this paper, which allows us to to optimally select the elements of the measurement protocol according to a predefined weighting between the variance of the estimated state parameters and... more
ABSTRACT An indirect measurement scheme is used in this paper, which allows us to to optimally select the elements of the measurement protocol according to a predefined weighting between the variance of the estimated state parameters and the ratio of the non-demolished copies of the system.
ABSTRACT There exists an extensive literature dealing with the estimation of reaction-rate constants of elementary reactions in complex chemical reactions, still it is not thoroughly investigated how to calculate statistically good... more
ABSTRACT There exists an extensive literature dealing with the estimation of reaction-rate constants of elementary reactions in complex chemical reactions, still it is not thoroughly investigated how to calculate statistically good estimates based upon a stochastic kinetic model. In the present paper a stochastic model is used and maximum likelihood estimates of the parameters are given utilizing the linear dependences of the right side of the kinetic differential equation on the reaction-rate constants. The present approach is capable of treating time continuous (as well as discrete) measurements. The explicit form of the estimate has been given in general cases and it has further been expanded in simple analytical cases. A physicochemical discussion of the applied model has also been included.
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A diagnostic expert system established on model-based reasoning for on-line diagnosis and loss prevention is described in the paper. Its diagnostic ”cause-effect” rules and possible actions (suggestions) are extracted from the results of... more
A diagnostic expert system established on model-based reasoning for on-line diagnosis and loss prevention is described in the paper. Its diagnostic ”cause-effect” rules and possible actions (suggestions) are extracted from the results of standard HAZOP analysis. Automatic focusing as well as ”what-if” type reasoning for testing hypothetical actions have been also implemented. The diagnostic system is tested on a granulator drum of a fertilizer plant in a simulation test-bed.
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Many lumped dynamic models of process systems can be written without approximation in quasi-polynomial differential-algebraic equation (QP-DAE) form. We propose nonlinearity indices for QP-DAEs based on the system invariants. There are... more
Many lumped dynamic models of process systems can be written without approximation in quasi-polynomial differential-algebraic equation (QP-DAE) form. We propose nonlinearity indices for QP-DAEs based on the system invariants. There are global (whole model) and local versions of the indices. The local ones are based on the implicit subsets (essential L-components) of the model. The indices are assessed against simulations
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The effect of model simplification assumptions on the differential index of DAE process models is investigated in this paper. Efficient incremental graph-theoretical algorithms are proposed to follow the changes in the variable-equation... more
The effect of model simplification assumptions on the differential index of DAE process models is investigated in this paper. Efficient incremental graph-theoretical algorithms are proposed to follow the changes in the variable-equation assignments during the modelling process. Case studies are used to demonstrate the operation of the algorithms and the effect of steady-state assumptions on the differential index of a simple process model.
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Causal knowledge in complex process systems is a powerful representational model that permits a range of important applications related to process risk management. These include the development of operator training systems, diagnosis... more
Causal knowledge in complex process systems is a powerful representational model that permits a range of important applications related to process risk management. These include the development of operator training systems, diagnosis tools, emergency response planning as well as implications on process and control system retrofit and design.Using a blended hazard identification approach we show how causal knowledge can be