Li-Ion Cell Aging Model Online Parameter Estimation for Improved Prognosis

2016 ◽  
Author(s):  
Punit Tulpule ◽  
Chin-Yao Chang ◽  
Giorgio Rizzoni
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Estimation Methods ◽  
Cell Aging ◽  
Model Parameters ◽  
Positive Electrodes ◽  
Aging Model ◽  
Estimation Algorithms

In this paper, a semi-empirical aging model of lithium-ion pouch cells containing blended spinel and layered-oxide positive electrodes is calibrated using aging campaigns. Sensitivity analysis is done on this model to identify the effect of parameter variations on the State of Health (SOH) prediction. The sensitivity analysis shows that the aging model alone is not robust enough to perform long term predictions, hence we propose to use online parameter estimation algorithms to adapt the model parameters. Four different estimation methods are compared using aging campaign. It is demonstrated that the estimation algorithms improve aging model leading to significant improvement in Remaining Useful Life (RUL) prediction.

scholarly journals A Stochastic Harmonic Oscillator Temperature Model for the Valuation of Weather Derivatives

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2890
Author(s):  
Alessio Giorgini ◽  
Rogemar S. Mamon ◽  
Marianito R. Rodrigo
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Harmonic Oscillator ◽  
Numerical Simulations ◽  
Model Parameters ◽  
Call Option ◽  
Temperature Model ◽  
Option Prices ◽  
Harmonic Oscillator Model ◽  
Temperature Dynamics

Stochastic processes are employed in this paper to capture the evolution of daily mean temperatures, with the goal of pricing temperature-based weather options. A stochastic harmonic oscillator model is proposed for the temperature dynamics and results of numerical simulations and parameter estimation are presented. The temperature model is used to price a one-month call option and a sensitivity analysis is undertaken to examine how call option prices are affected when the model parameters are varied.

Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

2021 ◽  
Author(s):  
Rodric Mérimé Nonki ◽  
André Lenouo ◽  
Christopher J. Lennard ◽  
Raphael M. Tshimanga ◽  
Clément Tchawoua
Keyword(s):  
Sensitivity Analysis ◽  
Impact Assessment ◽  
Hydrological Modeling ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Sensitivity Approach ◽  
Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

Parameter estimation using relay feedback

2019 ◽  
Vol 35 (4) ◽  
pp. 505-529
Author(s):  
Kalpana Dharmalingam ◽  
Thyagarajan Thangavelu
Keyword(s):  
Parameter Estimation ◽  
Closed Loop ◽  
Parametric Estimation ◽  
Estimation Methods ◽  
Model Parameters ◽  
Relay Feedback ◽  
Research Activity ◽  
Process Industries ◽  
Single Input Single Output ◽  
Estimation Techniques

Abstract In process industries, closed-loop step and closed-loop relay feedback tests are popularly used for estimating model parameters. In this paper, different methods available in the literature for parameter estimation using conventional techniques and techniques based on relay feedback test are surveyed by reviewing around 152 research articles published during the past three decades. Through a comprehensive survey of available literature, the parameter estimation methods are classified into two broad groups, namely conventional techniques and relay-based parametric estimation techniques. These relay-based techniques are further classified into two subgroups, namely single-input-single-output (SISO) systems and multi-input-multi-output systems (both square and nonsquare), and are revealed in a lucid manner with the help of benchmark examples and case studies. For the above categorized methods, the procedural steps involved in relay-based parametric estimation methods are also presented. To facilitate the readers, comparison tables are included to comprehend the results of different parametric estimation techniques available in the literature. The incorporation of quantitative and qualitative analysis of papers published in various journals in the above area with the help of pie charts and graphs would enable the readers to grasp the overview of the research activity being carried out in the relay feedback domain. At the end, the challenging issues in relay-based parametric estimation methods and the directions for future investigations that can be explored are also highlighted.

scholarly journals Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zheng Liu ◽  
Xuanju Dang ◽  
Hanxu Sun
Keyword(s):  
Moving Average ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
Parameters Identification ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
H Infinity ◽  
Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

Improvement of the simulation of the water and energy cycle using Multiscale Parameter Regionalization (MPR)

2020 ◽  
Author(s):  
Stephan Thober ◽  
Matthias Kelbling ◽  
Florian Pappenberger ◽  
Christel Prudhomme ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
High Resolution ◽  
Parameter Space ◽  
Land Surface ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Surface Model ◽  
Model Parameters ◽  
Environmental Models ◽  
Energy Cycle

<p>The representation of the water and energy cycle in environmental models is closely linked to the parameter values used in the process parametrizations. The dimension of the parameter space in spatially distributed environmental models corresponds to the number of grid cells multiplied by the number of parameters per grid cell. For large-scale simulations on national and continental scales, the dimensionality of the parameter space is too high for efficient parameter estimation using inverse estimation methods. A regularization of the parameter space is necessary to reduce its dimensionality. The Multiscale Parameter Regionalization (MPR) is one approach to achieve this.</p><p>MPR translates local geophysical properties into model parameters. It consists of two steps: 1) local high-resolution geophysical data sets (e.g. soil maps) are translated into model parameters using a transfer function. 2) the high-resolution model parameters are scaled to the model resolution using suitable upscaling operators (e.g., harmonic mean). The MPR technique was introduced into the mesoscale hydrologic model (mHM, Samaniego et al. 2010, Kumar et al. 2013) and it is key factor for its success on transferring parameters across scales and locations.  </p><p>In this study, we apply MPR to vegetation and soil parameters in the land surface model HTESSEL. This model is the land-surface component of the European Centre for Medium-Range Weather Forecasting seasonal forecasting system. About 100 hard-coded parameters have been extracted to allow for a comprehensive sensitivity analysis and parameter estimation.</p><p>We analyze simulated evaporation and runoff fluxes by HTESSEL using parameters estimated by MPR in comparison to a default HTESSEL setup over Europe. The magnitude of simulated long-term fluxes deviates the most (up to 10% and 20% for evapotranspiration and runoff, respectively) in regions with a large subgrid variability in geophysical attributes (e.g., soil texture). The choice of transfer functions and upscaling operators influences the magnitude of these differences and governs model performance assessed after calibration against observations (e.g. streamflow).</p><p><strong>References:</strong></p><p>Samaniego L., et al.  <strong>https://doi.org/10.1029/2008WR007327</strong></p><p>Kumar, R., et al.  <strong>https://doi.org/10.1029/2012WR012195</strong></p>

scholarly journals Parameter Identification and Sensitivity Analysis for Zero-Dimensional Physics-Based Lithium-Sulfur Battery Models

2021 ◽  
Vol 1 (4) ◽  
Author(s):  
Chu Xu ◽  
Timothy Cleary ◽  
Guoxing Li ◽  
Donghai Wang ◽  
Hosam Fathy
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Lithium Ion ◽  
Diffusion Reaction ◽  
Model Parameters ◽  
Data Sets ◽  
Diffusion Dynamics ◽  
Dimensional Models ◽  
Sulfur Battery ◽  
Lithium Sulfur

Abstract This paper examines parameter estimation for Lithium-Sulfur (Li-S) battery models from experimental data. Li-S batteries are attractive compared to traditional Lithium-ion batteries, thanks largely to their potential to achieve higher energy densities. The literature presents a number of Li-S battery models with varying fidelity and complexity levels. This includes both high-fidelity diffusion-reaction models as well as zero-dimensional models that neglect diffusion dynamics while capturing the underlying reduction-oxidation reaction physics. This paper focuses on four zero-dimensional models, representing different possible sets of redox reactions. There is a growing need for using experimental data sets to both parameterize and compare these models. To address this, Li-S coin cells were fabricated and tested. In parallel, a sensitivity analysis of key model parameters was conducted. Using this analysis, a subset of model parameters was selected for identification and estimation in all four Li-S battery models.

scholarly journals Data assimilation in integrated hydrological modelling in the presence of observation bias

2015 ◽  
Vol 12 (8) ◽  
pp. 8131-8173 ◽  
Author(s):  
J. Rasmussen ◽  
H. Madsen ◽  
K. H. Jensen ◽  
J. C. Refsgaard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Stream Flow ◽  
Groundwater Modeling ◽  
Optimal Parameter ◽  
Estimation Methods ◽  
Model Parameters ◽  
Stream Discharge ◽  
Groundwater Head ◽  
Observation Bias

Abstract. The use of bias-aware Kalman filters for estimating and correcting observation bias in groundwater head observations is evaluated using both synthetic and real observations. In the synthetic test, groundwater head observations with a constant bias and unbiased stream discharge observations are assimilated in a catchment scale integrated hydrological model with the aim of updating stream discharge and groundwater head, as well as several model parameters relating to both stream flow and groundwater modeling. The Colored Noise Kalman filter (ColKF) and the Separate bias Kalman filter (SepKF) are tested and evaluated for correcting the observation biases. The study found that both methods were able to estimate most of the biases and that using any of the two bias estimation methods resulted in significant improvements over using a bias-unaware Kalman Filter. While the convergence of the ColKF was significantly faster than the convergence of the SepKF, a much larger ensemble size was required as the estimation of biases would otherwise fail. Real observations of groundwater head and stream discharge were also assimilated, resulting in improved stream flow modeling in terms of an increased Nash-Sutcliffe coefficient while no clear improvement in groundwater head modeling was observed. Both the ColKF and the SepKF tended to underestimate the biases, which resulted in drifting model behavior and sub-optimal parameter estimation, but both methods provided better state updating and parameter estimation than using a bias-unaware filter.

scholarly journals Sensitivity analysis and parameter estimation for the distributed modeling of infiltration excess overland flow

2007 ◽  
Vol 4 (1) ◽  
pp. 363-405 ◽  
Author(s):  
W. Castaings ◽  
D. Dartus ◽  
F.-X. Le Dimet ◽  
G.-M. Saulnier
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Overland Flow ◽  
Flash Flood ◽  
Parametric Uncertainty ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Adjoint Sensitivity ◽  
Gradient Based ◽  
Infiltration Excess

Abstract. The variational methods widely used for other environmental systems are applied to a spatially distributed flash flood model coupling kinematic wave overland flow and Green Ampt infiltration. Using an idealized configuration where only parametric uncertainty is addressed, the potential of this approach is illustrated for sensitivity analysis and parameter estimation. Adjoint sensitivity analysis provides an extensive insight into the relation between model parameters and the hydrological response and enables the use of efficient gradient based optimization techniques.

scholarly journals Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3180 ◽  
Author(s):  
Bizhong Xia ◽  
Rui Huang ◽  
Zizhou Lao ◽  
Ruifeng Zhang ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
Least Square ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Temperature State

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

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