Perform Parameter Identification Research Articles

Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model

We consider the problem of optimally designing an excitation input for parameter identification of an electrochemical Li-ion battery model. The conventional approach to performing parameter identification uses standard test cycles. In contrast, we optimally design the input trajectory to maximize parameter identifiability in the sense of Fisher information. Specifically, we derive sensitivity equations for the electrochemical model. This approach enables parameter sensitivity analysis and optimal parameter fitting via gradient-based algorithms. This paper presents a general systematic approach to identify the electrochemical parameters in a non-invasive way. First, we group parameters into two sets: (i) equilibrium parameters, and (ii) dynamical parameters. We also divide the dynamical parameters into subsets by calculating orthogonalized sensitivity, which mitigates linear dependence between parameters. A large number of input profiles have been devised to constitute an input library. Then, the optimal inputs are selected from the input library to maximize the Fisher information, via convex programming. Using this framework a number of relevant experiments are obtained to parameterize. To validate our approach experimentally, we consider a 18650 Lithium nickel cobalt aluminum oxide battery. Compared to the conventional approach, our proposal achieves lower voltage RMSE across all experimental testing cycles.

A novel LLSDPso method for nonlinear dynamic parameter identification

PurposeThis paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors).Design/methodology/approachPrevious studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively.FindingsThe settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method.Originality/valueThe proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

Translation of the Single-Diode PV Model Parameters Identified by Using Explicit Formulas

Recent literature proposes some approaches that employ explicit equations for identifying the five parameters of the single-diode model describing a photovoltaic (PV) panel. These methods avoid the iterative solution of a nonlinear system of equations, whose convergence is very sensitive to the guess solution. Therefore, they are particularly suitable to perform parameter identification in real time and to be implemented on low-cost, low-performance processing platforms. In this paper, the applicability of some explicit methods, previously validated under standard test conditions, is analyzed for a large class of panels under operating conditions that are different from the standard ones. The study considers both a consolidated method for translating the PV model parameters as well as a novel approach. The analysis allows assessing the most suitable parameter translation equations for each considered explicit identification method, highlighting the effectiveness of such explicit approaches under different operating conditions. An in-depth validation based on experimental data concerning two commercial PV panels corroborates the analysis.

Hybrid parameter identification of a multi-modal underwater soft robot

We introduce an octopus-inspired, underwater, soft-bodied robot capable of performing waterborne pulsed-jet propulsion and benthic legged-locomotion. This vehicle consists for as much as 80% of its volume of rubber-like materials so that structural flexibility is exploited as a key element during both modes of locomotion. The high bodily softness, the unconventional morphology and the non-stationary nature of its propulsion mechanisms require dynamic characterization of this robot to be dealt with by ad hoc techniques. We perform parameter identification by resorting to a hybrid optimization approach where the characterization of the dual ambulatory strategies of the robot is performed in a segregated fashion. A least squares-based method coupled with a genetic algorithm-based method is employed for the swimming and the crawling phases, respectively. The outcomes bring evidence that compartmentalized parameter identification represents a viable protocol for multi-modal vehicles characterization. However, the use of static thrust recordings as the input signal in the dynamic determination of shape-changing self-propelled vehicles is responsible for the critical underestimation of the quadratic drag coefficient.

Inverse modeling of thin layer flow cells for detection of solubility, transport and reaction coefficients from experimental data

Thin layer flow cells are used in electrochemical research as experimental devices which allow to perform investigations of electrocatalytic surface reactions under controlled conditions using reasonably small electrolyte volumes. The paper introduces a general approach to simulate the complete cell using accurate numerical simulation of the coupled flow, transport and reaction processes in a flow cell. The approach is based on a mass conservative coupling of a divergence-free finite element method for fluid flow and a stable finite volume method for mass transport. It allows to perform stable and efficient forward simulations that comply with important physical constraints, namely mass conservation and maximum principles for the involved species. In this context, several recent approaches to obtain divergence-free velocity approximations from finite element simulations are discussed. In order to perform parameter identification, the forward simulation method is coupled to standard optimization tools. After an assessment of the inverse modeling approach using hypothetical, but realistic data, first results of the identification of solubility and transport data for O2 dissolved in organic electrolytes are presented. A plausibility study for a more complex situation with surface reactions concludes the paper and shows possible extensions of the presented numerical tools.

A single-variate building energy signature approach for periods with substantial solar gain

The use of regression analysis for the identification of building performance parameters based on measurements is often difficult due to collinearity between the outdoor temperature and the global solar radiation (S). This study proposes a method to overcome this issue. The proposed method is based on using the seasonal symmetry of S to pair data from time-periods equidistant from the winter solstice. In addition, a method to utilize synthetic data to fine-tune the paired-data approach is presented. To evaluate the paired-data approach, two years data from a multifamily building in Umeå was used to estimate the heat loss factor (air-to-air transmission including air leakage). The results were compared with results obtained when S was very low (S≈0). It was found that, the fine-tuned paired-data approach resulted in a modest deviation in the heat loss factor with an average absolute deviation of 4.0%. The small deviation indicates that the paired-data approach can extend the use of single-variate regression models for accurate identification of heat loss factors to situations where the solar gain is substantial. The paired-data approach was also used to calibrate a commercial energy building simulation tool.

Robust system identification and model predictions in the presence of systematic uncertainty

ContextModel-based data-interpretation techniques are increasingly used to improve the knowledge of complex system behavior. Physics-based models that are identified using measurement data are generally used for extrapolation to predict system behavior under other actions. In order to obtain accurate and reliable extrapolations, model-parameter identification needs to be robust in terms of variations of systematic modeling uncertainty introduced when modeling complex systems. Approaches such as Bayesian inference are widely used for system identification. More recently, error-domain model falsification (EDMF) has been shown to be useful for situations where little information is available to define the probability density function (PDF) of modeling errors. Model falsification is a discrete population methodology that is particularly suited to knowledge intensive tasks in open worlds, where uncertainty cannot be precisely defined. ObjectiveThis paper compares conventional uses of approaches such as Bayesian inference and EDMF in terms of parameter-identification robustness and extrapolation accuracy. MethodUsing Bayesian inference, three scenarios of conventional assumptions related to inclusion of modeling errors are evaluated for several model classes of a simple beam. These scenarios are compared with results obtained using EDMF. Bayesian model class selection is used to study the benefit of posterior model averaging on the accuracy of extrapolations. Finally, ease of representation and modification of knowledge is illustrated using an example of a full-scale bridge. ResultsThis study shows that EDMF leads to robust identification and more accurate predictions than conventional applications of Bayesian inference in the presence of systematic uncertainty. These results are illustrated with a full-scale bridge. This example shows that the engineering knowledge necessary to perform parameter identification and remaining-fatigue-life predictions of a complex civil structure is easily represented by the EDMF methodology. ConclusionModel classes describing complex systems should include two components: (1) unknown physical parameters that are identified using measurements; (2) conservative modeling error estimations that cannot be represented only as uncertainties related to physical parameters. In order to obtain accurate predictions, both components need to be included in the model-class definition. This study indicates that Bayesian model class selection may lead to over-confidence in certain model classes, resulting in biased extrapolation.

Interactive resistance chair to promote strengthening exercise in older adults.

We developed a strengthening exercise support system which can be remotely managed and clinically supervised via internet. Older adults may potentially benefit from such an exercise system however functionality of this system requires validation before commencement of field studies in older adults. The aim of this study was to introduce and assess validity of a prototype telerehabilitation system supporting computer-assisted home-based strengthening exercise. The system included a resistance chair with a set of movement and physiologic sensors. Real-time feedback on exercise performance was displayed on a touch screen dashboard. Personalized exercise parameters were managed by a rehabilitation team via a designated telerehabilitation site. Assessment of the system demonstrated sufficient validity in real-time identification of exercise performance and cardiovascular parameters. We concluded that the interactive resistance chair has a potential in promoting strengthening exercise and it is warranted for further evaluation in community dwelling older adults.

Air source heat pump water heaters in residential buildings in Australia: Identification of key performance parameters

Heat pump water heaters have been increasingly used to promote energy savings in residential buildings. Nonetheless, their performance may vary significantly due to different site-specific characteristics and technical specifications. To examine this issue, this research study sought to identify key performance parameters for air source heat pump water heaters (ASHPWH) in residential buildings in Australia by using a sensitivity analysis through theoretical models calibrated with empirical measurements. ASHPWH were analysed taking into account their energy performance (i.e. annual energy consumption and energy intensity) and level of service (i.e. compliance with recommended hot water temperatures). The assessment encompassed sensitivity analyses for key performance parameters related to the technical specification of ASHPWH (i.e. coefficients of performance, water heating capacities, hot water storage tank insulation, size and set-point temperature) and site-specific characteristics (i.e. weather conditions, cold water supply temperatures, hot water set-point temperatures and consumption patterns, and the time-distribution of energy according to the electricity tariffs). The findings indicate that the energy performance and the level of service of ASHPWH are mostly influenced by their coefficient of performance and site-specific electricity tariff, respectively. Therefore, careful consideration of ASHPWH technical specifications based on site-specific characteristics should be used to underpin the development of energy efficiency programmes for residential water heaters.

Parameter Identification Methods in a Model of the Cardiovascular System

To be clinically relevant, mathematical models have to be patient-specific, meaning that their parameters have to be identified from patient data. To achieve real time monitoring, it is important to select the best parameter identification method, in terms of speed, efficiency and reliability. This work presents a comparison of seven parameter identification methods applied to a lumped-parameter cardiovascular system model. The seven methods are tested using in silico and experimental reference data. To do so, precise formulae for initial parameter values first had to be developed. The test results indicate that the trust-region reflective method seems to be the best method for the present model. This method (and the proportional method) are able to perform parameter identification in two to three minutes, and will thus benefit cardiac and vascular monitoring applications.

Tilting-Pad Journal Bearings With Active Lubrication Applied as Calibrated Shakers: Theory and Experiment

In recent years, a continuous research effort has transformed the conventional tilting-pad journal bearing (TPJB) into a mechatronic machine element. The addition of electromechanical elements provides the possibility of generating controllable forces over the rotor as a function of a suitable control signal. Such forces can be applied in order to perform parameter identification procedures “in situ,” which enables evaluation of the mechanical condition of the machine in a noninvasive way. The usage of a controllable bearing as a calibrated shaker requires obtaining the bearing specific frequency dependent calibration function, i.e., the transfer function between control signal and force over the rotor. This work presents a theoretical model of the calibration function for a TPJB with active lubrication. The bearing generates controllable forces by injecting pressurized oil directly into the bearing clearance. The injected flow is controlled by means of a servovalve. The theoretical model includes the dynamics of the hydraulic system using a lumped parameter approach, which is coupled with the bearing oil film using a modified form of the Reynolds equation. The oil film model is formulated considering an elastothermohydrodynamic lubrication regime. New contributions to the mathematical modeling are presented, such as the inclusion of the dynamics of the hydraulic pipelines and the obtention of the bearing calibration function by means of harmonic analysis of a linearized form of the controllable bearing constitutive equations. The mathematical model is used to study the relevance and effects of different parameters on the calibration function, aiming at providing general guidelines for the active bearing design. Finally, experimental results regarding the calibration function and the usage of the studied bearing as a calibrated shaker provide insight into the possibilities of application of this technology.

The neuronal response at extended timescales: a linearized spiking input-output relation.

Many biological systems are modulated by unknown slow processes. This can severely hinder analysis – especially in excitable neurons, which are highly non-linear and stochastic systems. We show the analysis simplifies considerably if the input matches the sparse “spiky” nature of the output. In this case, a linearized spiking Input–Output (I/O) relation can be derived semi-analytically, relating input spike trains to output spikes based on known biophysical properties. Using this I/O relation we obtain closed-form expressions for all second order statistics (input – internal state – output correlations and spectra), construct optimal linear estimators for the neuronal response and internal state and perform parameter identification. These results are guaranteed to hold, for a general stochastic biophysical neuron model, with only a few assumptions (mainly, timescale separation). We numerically test the resulting expressions for various models, and show that they hold well, even in cases where our assumptions fail to hold. In a companion paper we demonstrate how this approach enables us to fit a biophysical neuron model so it reproduces experimentally observed temporal firing statistics on days-long experiments.

Merchandisers’ Performance and Supply Chain Competitiveness in Apparel Export Units

Purpose: The purpose of this research is to explore the various attributes of merchandisers' performance at organisation level and parameters of supply chain competitiveness that affect the apparel industry. Design/methodology/approach: The study has focussed on apparel export companies located in NCR of Delhi. The primary data has been collected through structured questionnaire. The areas covered in questionnaire encompassed aspects of merchandisers' job responsibilities and apparel supply chain competitiveness factors as relevant to apparel industry. Findings: Organisation and Communication skills, knowledge of apparel manufacturing and quality assurance, knowledge of dynamic product costing have a strong association with supply chain competitiveness parameters of product quality, product delivery and retail pricing. Training of merchandisers in the areas which affect supply chain competitiveness of apparel export units is of key importance. Research Limitations/implications: The recommendations have been made for apparel export companies as well as for individual merchandisers. The recommendations primarily include education and training of merchandisers, standardising documentation requirements in the company and having a Standard Operation Procedure (SOP), strengthening of Information and Communication Technology (ICT) and apparel manufacturing systems, performance appraisal with respect to Key Result Areas (KRAs) and training by buyers. The paper opens up scope for apparel industry and organisation specific studies. Originality/value: Identification of merchandiser performance and supply chain competitiveness parameters in apparel industry is based on extensive literature survey supplemented by companies' direct primary responses through a structured questionnaire, which were associated through correlation analysis. Keywords- Merchandiser, Supply Chain Competitiveness, Skill, Merchandisers' Performance

Microwave inactivation of Escherichia coli K12 CIP 54.117 in a gel medium: Experimental and numerical study

This study evaluates the efficiency of the inactivation of Escherichia coli K12, entrapped within calcium alginate gel, by microwave processing compared to a conventional approach i.e. by heating in a water bath. Microbial thermal inactivation equations coupled with heat transfer and Maxwell’s equations are integrated into a 3D Finite Elements model under dynamic heating conditions. Water bath microbial inactivation experimental data are exploited for performing parameter identification of a non-linear microbial model, and the Calcium alginate gel’s dielectric properties were numerically estimated. The coupled model provides a very good fitting to the experimental results. The simulation have shown uneven temperature distribution during microwave heating which may interpret its lower inactivation efficiency comparing to the conventional water bath treatment. This study also demonstrates the reliability of the coupled modeling approach to estimate the efficiency of the microbial inactivation, despite the thermal heterogeneity inherent in the microwave treatment.

Modelling of spatial contaminant probabilities of occurrence of chlorinated hydrocarbons in an urban aquifer

In this study, a 3D urban groundwater model is presented which serves for calculation of multispecies contaminant transport in the subsurface on the regional scale. The total model consists of two submodels, the groundwater flow and reactive transport model, and is validated against field data. The model equations are solved applying finite element method. A sensitivity analysis is carried out to perform parameter identification of flow, transport and reaction processes. Coming from the latter, stochastic variation of flow, transport, and reaction input parameters and Monte Carlo simulation are used in calculating probabilities of pollutant occurrence in the domain. These probabilities could be part of determining future spots of contamination and their measure of damages. Application and validation is exemplarily shown for a contaminated site in Braunschweig (Germany), where a vast plume of chlorinated ethenes pollutes the groundwater. With respect to field application, the methods used for modelling reveal feasible and helpful tools to assess natural attenuation (MNA) and the risk that might be reduced by remediation actions.

Identification of key performance parameters during off-spin bowling with a smart cricket ball

A smart cricket ball, instrumented with three high-speed MEMS gyroscopes, was used to measure performance parameters during off-spin bowling. The average spin rate and finger torque generated by the bowler were 22.7 rps and 0.28 N·m, respectively. The arm motion could be clearly identified on the spin-axis vector diagram. The spin-axis vector described a loop that closely followed the orientation of the bowler's forearm. The centre of pressure (COP) of the finger force was located between the ring and the middle finger. A set of performance parameters was identified in this study that can be used to quantify off-spin bowling performance in the future. These performance parameters are: the spin rate of the ball, the position of the spin axis with respect to the plane of the seam, the amount of torque applied to the ball by the bowler, the bowler's angular arm velocity, the finger's COP (where the force is applied to the ball) and the impact point on the ball.

Medizinische und leistungsdiagnostische Kenngrößen im nordischen Skisport

Summary Nordic skiers are subject to highest demands, amplified by the increasing professionalism and the continuously rising level of performance. To ensure ideal health care and coaching, an enlargement and thus improvement of sports medicine and scientific knowledge is required. For identification of medical and performance characteristics, properties and parameters, data from 429 elite nordic ski athletes (cross country skiing, biathlon, nordic combination, ski jumping) were cross-sectional and longitudinal analysed. The comparison between sport, gender, age und performance level showed significant differences. By creating such particular sports medicine profiles, realistic assessments of results in age-matched comparison, both in terms of health-related, as well as performance-related tests, are prospective possible.

Frequency-Domain Identification of Linear Time-Periodic Systems Using LTI Techniques

A variety of systems can be faithfully modeled as linear with coefficients that vary periodically with time or linear time-periodic (LTP). Examples include anisotropic rotor-bearing systems, wind turbines, and nonlinear systems linearized about a periodic trajectory. Many of these have been treated analytically in the literature, yet few methods exist for experimentally characterizing LTP systems. This paper presents a set of tools that can be used to identify a parametric model of a LTP system, using a frequency-domain approach and employing existing algorithms to perform parameter identification. One of the approaches is based on lifting the response to obtain an equivalent linear time-invariant (LTI) form and the other based is on Fourier series expansion. The development focuses on the preprocessing steps needed to apply LTI identification to the measurements, the postprocessing needed to reconstruct the LTP model from the identification results, and the interpretation of the measurements. This elucidates the similarities between LTP and LTI identification, allowing the experimentalist to transfer insight between the two. The approach determines the model order of the system and the postprocessing reveals the shapes of the time-periodic functions comprising the LTP model. Further postprocessing is also presented, which allows one to generate the state transition and time-varying state matrices of the system from the output of the LTI identification routine, so long as the measurement set is adequate. The experimental techniques are demonstrated on simulated measurements from a Jeffcott rotor mounted on an anisotropic flexible shaft supported by anisotropic bearings.

Identification of non-linear microbial inactivation kinetics under dynamic conditions

In this study dynamic microbial inactivation experiments are exploited for performing parameter identification of a non-linear microbial model. For that purpose microbial inactivation data are produced and a differential equation exhibiting a shoulder and a loglinear phase is employed. The derived parameter estimates from this method were used to perform predictions on an independent experimental set at fluctuating temperature. Joint confidence regions and asymptotic confidence intervals of the estimated parameters were compared with previous studies originating from parameter identification under isothermal conditions. The developed approach can provide more reliable estimates for realistic conditions compared to the usual or standard two step approach.

PLAN VIEW PATTERN CONTROL FOR STEEL PLATES USING LOCALLY-WEIGHTED REGRESSION

A technique for performing parameter identification in a locally-weighted regression model using foresight information on the physical properties of the object of interest as constraints was proposed. This method was applied to plan view pattern control of steel plates, and a reduction of shape nonconformity (crop) at the plate head end was confirmed.