scholarly journals Battery Model Parameters Estimation Using Simulated Annealing

2017 ◽  
Vol 18 (1) ◽  
pp. 127 ◽  
Author(s):  
Marcia De Fatima Brondani ◽  
Airam Teresa Zago Romcy Sausen ◽  
Paulo Sérgio Sausen ◽  
Manuel Osório Binelo
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Simulated Annealing ◽  
Visual Analysis ◽  
Lithium Ion ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Algorithm Efficiency ◽  
Battery Model ◽  
Discharge Curves

In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

scholarly journals Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm

2019 ◽  
Vol 20 (1) ◽  
pp. 149 ◽  
Author(s):  
Marcia De Fatima Brondani Binelo ◽  
Airam Teresa Zago Romcy Sausen ◽  
Paulo Sérgio Sausen ◽  
Manuel Osório Binelo
Keyword(s):  
Mathematical Modeling ◽  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Visual Analysis ◽  
Lithium Ion ◽  
Electrical Model ◽  
Pulsed Discharge ◽  
Battery Lifetime ◽  
Agile Process ◽  
Discharge Curves

In this paper, a parametrization methodology based on the Genetic Algorithm meta-heuristic is proposed for the Chen and Rincón-Mora model parameter estimation, this model is used for the mathematical modeling of the Lithium-ion Polymer batteries lifetime. The model is also parametrized using the conventional procedures, which is based on the visual analysis of pulsed discharge curves, as presented in the literature. For both parametrization procedures, and for the model validation, experimental data obtained from a platform test are used. The results show that the proposed Genetic Algorithm is able to parametrize the model with better efficacy, presenting lower mean error, and also is a more agile process than the conventional one, been less subject to subjective aspects.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

scholarly journals Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Kai Zhang ◽  
Jian Ma ◽  
Xuan Zhao ◽  
Xiaodong Liu ◽  
Yixi Zhang
Keyword(s):  
Parameters Estimation ◽  
State Of Charge ◽  
Superior Performance ◽  
Model Parameters ◽  
Local Optimum ◽  
Soc Estimation ◽  
Chaotic Mapping ◽  
Battery Model ◽  
Ant Lion Optimizer ◽  
Ant Lion

For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.

Modeling and Simulation Research on Lithium-Ion Battery in Electric Vehicles Based on Genetic Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 246-249
Author(s):  
Cheng Lin ◽  
Xiao Hua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Lithium Ion Battery ◽  
Stress Test ◽  
Lithium Ion ◽  
Model Parameters ◽  
Test Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Simulation Research ◽  
Battery Model

Based on the genetic algorithm (GA), a novel type of parameters identification method on battery model was proposed. The battery model parameters were optimized by genetic optimization algorithm and the other parameters were identified through the hybrid pulse power characterization (HPPC) test. Accuracy and efficiency of the battery model were validated with the dynamic stress test (DST). Simulation and experiment results shows that the proposed model of the lithium-ion battery with identified parameters was accurate enough to meet the requirements of the state of charge (SoC) estimation and battery management system.

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 Advanced Lithium-Ion Battery Model for Power System Performance Analysis

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2411 ◽  
Author(s):  
Szymon Potrykus ◽  
Filip Kutt ◽  
Janusz Nieznański ◽  
Francisco Jesús Fernández Morales
Keyword(s):  
Lithium Ion Battery ◽  
System Performance ◽  
System Development ◽  
Storage System ◽  
Lithium Ion ◽  
Model Parameters ◽  
Diffusion Resistance ◽  
System Modelling ◽  
Battery Management System ◽  
Battery Model

The paper describes a novel approach in battery storage system modelling. Different types of lithium-ion batteries exhibit differences in performance due to the battery anode and cathode materials being the determining factors in the storage system performance. Because of this, the influence of model parameters on the model accuracy can be different for different battery types. These models are used in battery management system development for increasing the accuracy of SoC and SoH estimation. The model proposed in this work is based on Tremblay model of the lithium-ion battery. The novelty of the model lies in the approach used for parameter estimation as a function of battery physical properties. To make the model perform more accurately, the diffusion resistance dependency on the battery current and the Peukert effect were also included in the model. The proposed battery model was validated using laboratory measurements with a LG JP 1.5 lithium-ion battery. Additionally, the proposed model incorporates the influence of the battery charge and discharge current level on battery performance.

UAS based Li-ion battery model parameters estimation

2017 ◽  
Vol 66 ◽  
pp. 126-145 ◽  
Author(s):  
D. Ali ◽  
S. Mukhopadhyay ◽  
H. Rehman ◽  
A. Khurram
Keyword(s):  
Parameters Estimation ◽  
Model Parameters ◽  
Li Ion Battery ◽  
Battery Model ◽  
Li Ion

scholarly journals Parameters Estimation of the Rakhmatov and Vrudhula Model from the Optimization Method Search in Improved Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
B. F. Silva ◽  
P. S. Sausen ◽  
A. Sausen
Keyword(s):  
Experimental Data ◽  
Comparative Analysis ◽  
Cell Phone ◽  
Simulated Data ◽  
Optimization Methods ◽  
Optimization Method ◽  
Parameters Estimation ◽  
Model Accuracy ◽  
Battery Model ◽  
Network Search

This paper presents a proposition of a new optimization method called Search in Improved Network, which is an extension of the method Search in Modified Network, to calculate the empirical parameters of the Rakhmatov and Vrudhula model for predicting batteries lifetime used in mobile devices. The new method is evaluated according to the following methodology. At first empirical parameters are computed considering the optimization methods Search in Improved Network, Search in Modified Network, and Least Squares, as well as the experimental data obtained from a testbed, considering a Lhition-Ion battery, model BL5F, used in the Nokia N95 cell phone. In a second moment, the Rakhmatov and Vrudhula model is validated for each set of parameters obtained, and the simulated data from the model are compared with a set of experimental data. From simulations results a comparative analysis is performed and it is found that by the application of the method Search in Improved Network in the parameters estimation of the Rakhmatov and Vrudhula model it is possible to obtain an easy and intuitive implementation, improving the results obtained in the model accuracy, as well as preserving the runtime.

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