scholarly journals Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1205 ◽  
Author(s):  
Pingwei Gu ◽  
Zhongkai Zhou ◽  
Shaofei Qu ◽  
Chenghui Zhang ◽  
Bin Duan
Keyword(s):  
State Estimation ◽  
Sampling Frequency ◽  
Operating Conditions ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Selection Strategy ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Battery Modeling ◽  
The Relationship

Battery characterization data is the basis for battery modeling and state estimation. It is generally believed that the higher the sampling frequency, the finer the data, and the higher the model and state estimation accuracy. However, scientific selection strategy for sampling frequency is very important but rarely studied. This paper studies the influence of sampling frequency on the accuracy of battery model and state estimation under four different sampling frequencies: 0.2 Hz, 1 Hz, 2 Hz, and 10 Hz. Then, a function is proposed to depict the relationship between accuracy and sampling frequency, which shows an optimal selection principle. The iterative identification algorithm is presented to identify the model parameters, and state-of-charge (SOC) is estimated via extended Kalman filter algorithm. Experimental results with different operating conditions clearly show the relationship between sampling frequency, accuracy, and data quantity, and the proposed selection strategy has high practical value and universality.

scholarly journals Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satoshi Miyamoto ◽  
Zu Soh ◽  
Shigeyuki Okahara ◽  
Akira Furui ◽  
Taiichi Takasaki ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Flow Rate ◽  
Neurological Complications ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Perfusion Flow ◽  
Postsurgical Complication ◽  
Tenfold Cross Validation ◽  
Suction Flow ◽  
The Relationship

AbstractThe need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.

scholarly journals Characterization and Evolution of a Digital Economy Ecosystem Based on an Interspecies Competition Model

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Han Zhou ◽  
Xiaorui Han ◽  
Le Wang
Keyword(s):  
Decision Making ◽  
Equilibrium Point ◽  
Digital Economy ◽  
Operating Conditions ◽  
Evolution Model ◽  
Dynamic Evolution ◽  
Model Parameters ◽  
Entrepreneurial Ecosystem ◽  
Interspecies Competition ◽  
The Relationship

This paper provides an in-depth study and analysis of the characterization of the digital economy ecosystem and the mechanism of eye-flowering through the method of interspecies competition. The evolutionary game model of symbiotic decision-making in the entrepreneurial ecosystem is constructed, the evolutionary process of symbiotic decision-making of subjects is analyzed through mathematical derivation, and the symbiotic decision-making process of subjects is simulated through computer simulation to answer how the subjects of the entrepreneurial ecosystem make symbiotic decisions and explore the mechanism of symbiotic formation of the entrepreneurial ecosystem. Then, based on the ecological perspective, the symbiotic evolution model of entrepreneurial ecosystem subjects is constructed from the subject level, the equilibrium point of the evolution of entrepreneurial ecosystem subjects, the stability conditions, and the relationship between the equilibrium point and the symbiosis model are analyzed, and the symbiotic evolution paths of entrepreneurial ecosystem subjects under different symbiosis modes, initial population size, maximum size, and natural growth rate are presented with simulation experiments, respectively. The main characteristics and manifestations of the dynamic evolution of the platform ecosystem are analyzed, and the key competitive factors that determine the dynamic evolution of the platform ecosystem are depicted. Then, according to the inherent characteristic laws of the platform ecosystem, the complex network approach is applied to construct a dynamic evolution model with originality and wide applicability for the change of bilateral user scale. Based on the dynamic evolution process, the relationship between model parameters and business performance is explored, and the trajectory of bilateral user size change over time and the range of parameters are derived by numerical calculation. Finally, using Monte Carlo simulation methods, the dynamic evolution model is used to predict the future operating conditions of platform enterprises, providing a valuation basis for investors to make investment decisions and helping platform managers to formulate business strategies.

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

Development of State Estimation Filter Simulator Built on an Integrated GUI Framework

2016 ◽  
Vol 20 (5) ◽  
pp. 721-729
Author(s):  
Masaru Morita ◽  
◽  
Takeshi Nishida
Keyword(s):  
Time Series ◽  
User Interface ◽  
State Estimation ◽  
Graphical User Interface ◽  
Kalman Filters ◽  
Point Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
System Designer ◽  
The Relationship

We have developed a graphical user interface (GUI)-based state estimation filter simulator (called StefAny) that makes it easy to understand and compare the behaviors of filters such as Kalman filters (KFs) and particle filters (PFs). The key feature of StefAny is to show, when a system designer applies a PF, a detailed graph representing the relationship among the distribution and weights of all particles on any arbitrary timeline through simulation. Moreover, the timeline can be specified on another graph showing an estimated time series for each filter. These features enable system designers to easily check the compatibility between a filter and a target distribution, which determines the state estimation accuracy. In this paper, we present the functions of StefAny and demonstrate in detail how StefAny facilitates understanding of the properties of filters via a compatibility check comparison experiment for PFs, point estimation methods, and distributions.

scholarly journals Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics

2020 ◽  
Vol 11 (3) ◽  
pp. 50 ◽  
Author(s):  
Zachary Bosire Omariba ◽  
Lijun Zhang ◽  
Hanwen Kang ◽  
Dongbai Sun
Keyword(s):  
Parameter Identification ◽  
State Estimation ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Model ◽  
Temperature Dynamics

There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges in their development, necessitating research in this area. For the safe operation of lithium-ion batteries, state estimation is very significant and battery parameter identification is the core in battery state estimation. The battery management system for electric vehicle application must perform a few estimation tasks in real-time. Battery state estimation is defined by the battery model adopted and its accuracy impacts the accuracy of state estimation. The knowledge of the actual operating conditions of electric vehicles requires the application of an accurate battery model; for our research, we adopted the use of the dual extended Kalman filter and it demonstrated that it yields more accurate and robust state estimation results. Since no single battery model can satisfy all the requirements of battery estimation and parameter identification, the hybridization of battery models together with the introduction of internal sensors to batteries to measure battery internal reactions is very essential. Similarly, since the current battery models rarely consider the coupling effect of vibration and temperature dynamics on model parameters during state estimation, this research goal is to identify the battery parameters and then present the effect of the vibration and temperature dynamics in battery state estimation.

scholarly journals Thermal Analysis of LMO/Graphite Batteries Using Equivalent Circuit Models

Batteries ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 58
Author(s):  
Nadjiba Mahfoudi ◽  
M’hamed Boutaous ◽  
Shihe Xin ◽  
Serge Buathier
Keyword(s):  
Thermal Behavior ◽  
Equivalent Circuit ◽  
Thermal Management ◽  
Dynamic Models ◽  
Operating Time ◽  
Operating Conditions ◽  
State Of Charge ◽  
Model Parameters ◽  
Battery Model ◽  
Equivalent Circuit Models

An efficient thermal management system (TMS) of electric vehicles requires a high-fidelity battery model. The model should be able to predict the electro-thermal behavior of the battery, considering the operating conditions throughout the battery’s lifespan. In addition, the model should be easy to handle for the online monitoring and control of the TMS. Equivalent circuit models (ECMs) are widely used because of their simplicity and suitable performance. In this paper, the electro-thermal behavior of a prismatic 50 Ah LMO/Graphite cell is investigated. A dynamic model is adopted to describe the battery voltage, current, and heat generation. The battery model parameters are identified for a single cell, considering their evolution versus the state of charge and temperature. The needed experimental data are issued from the measurements carried out, thanks to a special custom electrical bench able to impose a predefined current evolution or driving cycles, controllable by serial interface. The proposed battery parameters, functions of state of charge (SOC), and temperature (T) constitute a set of interesting and complete data, not available in the literature, and suitable for further investigations. The thermal behavior and the dynamic models are validated using the New European Driving Cycle (NEDC), with a large operating time, higher than 3 h. The measurement and model prediction exhibit a temperature difference less than 1.2 °C and a voltage deviation less than 3%, showing that the proposed model accurately predicts current, voltage, and temperature. The combined effects of temperature and SOC provides a more efficient modeling of the cell behavior. Nevertheless, the simplified model with only temperature dependency remains acceptable. Hence, the present modeling constitutes a confident prediction and a real step for an online control of the complete thermal management of electrical vehicles.

Online Estimation Algorithm for a Biaxial Ankle Kinematic Model With Configuration Dependent Joint Axes

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Y. H. Tsoi ◽  
S. Q. Xie
Keyword(s):  
Kinematic Model ◽  
Estimation Algorithm ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Extended Model ◽  
Identification Algorithm ◽  
Kinematic Parameters ◽  
Rls Algorithm

The kinematics of the human ankle is commonly modeled as a biaxial hinge joint model. However, significant variations in axis orientations have been found between different individuals and also between different foot configurations. For ankle rehabilitation robots, information regarding the ankle kinematic parameters can be used to estimate the ankle and subtalar joint displacements. This can in turn be used as auxiliary variables in adaptive control schemes to allow modification of the robot stiffness and damping parameters to reduce the forces applied at stiffer foot configurations. Due to the large variations observed in the ankle kinematic parameters, an online identification algorithm is required to provide estimates of the model parameters. An online parameter estimation routine based on the recursive least-squares (RLS) algorithm was therefore developed in this research. An extension of the conventional biaxial ankle kinematic model, which allows variation in axis orientations with different foot configurations had also been developed and utilized in the estimation algorithm. Simulation results showed that use of the extended model in the online algorithm is effective in capturing the foot orientation of a biaxial ankle model with variable joint axis orientations. Experimental results had also shown that a modified RLS algorithm that penalizes a deviation of model parameters from their nominal values can be used to obtain more realistic parameter estimates while maintaining a level of estimation accuracy comparable to that of the conventional RLS routine.

scholarly journals Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2939 ◽  
Author(s):  
Bizhong Xia ◽  
Guanghao Chen ◽  
Jie Zhou ◽  
Yadi Yang ◽  
Rui Huang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Joint Estimation ◽  
Estimation Accuracy ◽  
Degree Of Polarization ◽  
State Of Health ◽  
Battery Capacity

The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.

scholarly journals Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6284
Author(s):  
Fan Zhang ◽  
Lele Yin ◽  
Jianqiang Kang
Keyword(s):  
Kalman Filter ◽  
Adaptive Estimation ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Adaptive Kalman Filter ◽  
Initial Errors ◽  
Noise Covariance ◽  
Filter Algorithms

The traditional Kalman filter algorithms have disadvantages of poor stability (the program cannot converge or crash), robustness (sensitive to the initial errors) and accuracy, partially resulted from the fact that noise covariance matrices in the algorithms need to be set artificially. To overcome the above problems, some adaptive Kalman filter (AKF) algorithms are studied, but the problems still remain unsolved. In this study, two improved AKF algorithms, the improved Sage-Husa and innovation-based adaptive estimation (IAE) algorithms, are proposed. Under the different operating conditions, the estimation accuracy, filter stability, and robustness of the two proposed algorithms are analyzed. Results show that the state of charge (SOC) Max error based on the improved Sage-Husa and the improved IAE is less than 3% and 1.5%, respectively, while the Max errors of the original algorithms is larger than 16% and 4% The two proposed algorithms have higher filter stability than the traditional algorithms. In addition, analyses of the robustness of the two proposed algorithms are carried out by changing the initial parameters, proving that neither are sensitive to the initial errors.

scholarly journals Análisis de una serie de tiempo utilizando diseño de experimentos como herramienta de calibración

2015 ◽  
Vol 6 (1) ◽  
pp. 50 ◽  
Author(s):  
Raúl Prada-Núñez ◽  
Cesar Augusto Hernández-Suárez
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Model Parameters ◽  
Arima Models ◽  
Deterministic Models ◽  
Relationship Of ◽  
The Relationship ◽  
Over Time

ResumenLas series temporales se usan para estudiar la relación de una variable consigo misma a lo largo del tiempo en intervalos regulares; se consideró el consumo energético de España durante una muestra de 5 días, recurriendo a diversos modelos deterministas se buscaba modelar su comportamiento de la forma más ajustada. Se utiliza el diseño de experimentos para calibrar los parámetros del modelo de HoltWinters validando aquellos efectos que resultan significativos en la minimización del MAPE, con el fin de identificar las Condiciones Operativas Óptimas del modelo. Por último, se evaluan diversos modelos ARIMA aplicados a los residuos obtenidos del modelo de Holt Winters para convertirlo en ruido blanco, utilizando la metodología Box-Jenkins.Palabras claves: modelo Holt-Winters, modelos ARIMA, Series de tiempo. AbstractTime series are used to study the relationship of a variable with itself over time at regular intervals. Energy consumption in Spain was considered for a sample of five days, using various deterministic models sought to model their behavior in the most accurate way. The design of experiments is used to calibrate the model parameters Holt-Winters validating those effects that are significant in minimizing MAPE,in order to identify the optimum operating conditions of the model. Finally, various ARIMA models applied to residues obtained from Holt-Winters model to make it white noise, using the Box-Jenkins methodology are evaluated.Keywords:  Holt-Winters model, ARIMA models, Time series.

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