Development and Experimental Parameterization of a Physics-Based Second-Order Lithium-Ion Battery Model

2014 ◽  
Author(s):  
Donald Docimo ◽  
Mohammad Ghanaatpishe ◽  
Hosam K. Fathy
Keyword(s):  
Model Reduction ◽  
Lithium Ion Battery ◽  
Control Design ◽  
Lithium Ion ◽  
Second Order ◽  
Experimental Results ◽  
Model Parameters ◽  
Order Model ◽  
Battery Model ◽  
Analytic Expression

This paper uses the principles of electrochemistry to derive a simple second-order model of lithium-ion battery dynamics. Low-order lithium-ion battery models exist in the literature, but are typically either linear, empirical, or both. Our goal, in contrast, is to obtain a model simple enough for control design but grounded in the principles of electrochemistry. The model reduction approach used in this paper has the added advantage of leading to a novel analytic expression for the capacitance associated with voltage relaxation. A process for identifying model parameters from experiments is outlined, and experimental results are used to evaluate the validity of the model.

Model Identification of Power Lithium-Ion Battery Based on Laplace Transform

2014 ◽  
Vol 953-954 ◽  
pp. 775-779
Author(s):  
En Guang Hou ◽  
Xin Qiao ◽  
Guang Min Liu
Keyword(s):  
Laplace Transform ◽  
Lithium Ion Battery ◽  
Equivalent Circuit ◽  
Least Squares Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Second Order ◽  
Equivalent Model ◽  
Model Parameters ◽  
Test Model

In allusion to nonlinear characteristic of power lithium-ion battery, presented a method for identifying of power lithium-ion battery based on Laplace transform. First, analyzed the characteristics of the equivalent circuit model of power lithium-ion battery, determined the model of second-order RC equivalent circuit; Second, established equation of second-order RC equivalent circuit and conducted a Laplace transform; Third,using the massive data of charge-discharge test,model parameters was identified by least-squares method; Simulation results show that the method can effectively identify the equivalent model parameters, and model parameters small error.

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 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.

scholarly journals Online parameter estimation of a lithium-ion battery based on sunflower optimization algorithm

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Mouncef Elmarghichi ◽  
Mostafa Bouzi ◽  
Naoufl Ettalabi
Keyword(s):  
Lithium Ion Battery ◽  
Optimization Algorithm ◽  
Estimation Error ◽  
Stress Test ◽  
Lithium Ion ◽  
Identification Accuracy ◽  
Recursive Least Squares ◽  
Equivalent Model ◽  
Model Parameters ◽  
Battery Model

For techniques used to estimate battery state of charge (SOC) based on equivalent electric circuit models (ECMs), the battery equivalent model parameters are affected by factors such as SOC, temperature, battery aging, leading to SOC estimation error. Therefore, it is necessary to accurately identify these parameters. Updating battery model parameters constantly also known as online parameter identification can effectively solve this issue. In this paper, we propose a novel strategy based on the sunflower optimization algorithm (SFO) to identify battery model parameters and predict the output voltage in real-time. The identification accuracy has been confirmed using empirical data obtained from CALCE battery group (the center for advanced life cycle engineering) performed on the Samsung (INR 18650 20R) battery cell under one electric vehicle (EV) cycle protocol named dynamic stress test. Comparative analysis of SFO and AFRRLS (adaptive forgetting factor of recursive least squares) is carried out to prove the efficiency of the proposed algorithm. Results show that the calibrated model using SFO has superiority compared with AFFRLS algorithm to simulate the dynamic voltage behavior of a lithium-ion battery in EV application.

scholarly journals Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters

Batteries ◽  
2016 ◽  
Vol 2 (2) ◽  
pp. 13 ◽  
Author(s):  
Kotub Uddin ◽  
Surak Perera ◽  
W. Widanage ◽  
Limhi Somerville ◽  
James Marco
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Model Parameters ◽  
Battery Model ◽  
Battery Degradation ◽  
Electrochemical Battery

scholarly journals Scale-up of lithium-ion battery model parameters from cell level to module level – identification of current issues

2017 ◽  
Vol 138 ◽  
pp. 223-228 ◽  
Author(s):  
Anup Barai ◽  
T.R. Ashwin ◽  
Christos Iraklis ◽  
Andrew McGordon ◽  
Paul Jennings
Keyword(s):  
Lithium Ion Battery ◽  
Scale Up ◽  
Lithium Ion ◽  
Model Parameters ◽  
Cell Level ◽  
Battery Model ◽  
Level Identification

scholarly journals Parameter and Order Identification of Fractional Systems with Application to a Lithium-Ion Battery

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1607
Author(s):  
Oliver Stark ◽  
Martin Pfeifer ◽  
Sören Hohmann
Keyword(s):  
Lithium Ion Battery ◽  
Output Signal ◽  
Function Method ◽  
Lithium Ion ◽  
Experimental Results ◽  
Fractional Systems ◽  
Fractional Model ◽  
Battery Model ◽  
Identification Method ◽  
Practical Usefulness

This paper deals with a method for the parameter and order identification of a fractional model. In contrast to existing approaches that can either handle noisy observations of the output signal or systems that are not at rest, the proposed method does not have to compromise between these two characteristics. To handle systems that are not at rest, the parameter, as well as the order identification, are based on the modulating function method. The novelty of the proposed method is that an optimization-based approach is used for the order identification. Thus, even if only noisy observations of the output signal are available, an approximate identification can be performed. The proposed identification method is, then, applied to identify the parameters and orders of a lithium-ion battery model. The experimental results illustrate the practical usefulness and verify the validity of our approach.

Maximizing Parameter Identifiability of an Equivalent-Circuit Battery Model Using Optimal Periodic Input Shaping

2014 ◽  
Author(s):  
Michael J. Rothenberger ◽  
Joel Anstrom ◽  
Sean Brennan ◽  
Hosam K. Fathy
Keyword(s):  
Lithium Ion Battery ◽  
Equivalent Circuit ◽  
Trajectory Optimization ◽  
Lithium Ion ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Input Shaping ◽  
Parameter Identifiability ◽  
Battery Model ◽  
Periodic Input

This paper shapes the periodic cycling of a lithium-ion battery to maximize the battery’s parameter identifiability. The paper is motivated by the need for faster and more accurate lithium-ion battery diagnostics, especially for transportation. Poor battery parameter identifiability makes diagnostics challenging. The existing literature addresses this challenge by using Fisher information to quantify battery parameter identifiability, and showing that test trajectory optimization can improve identifiability. One limitation is this literature’s focus on offline estimation of battery model parameters from multi-cell laboratory cycling tests. This paper is motivated, in contrast, by online health estimation for a target battery or cell. The paper examines this “targeted estimation” problem for both linear and nonlinear second-order equivalent-circuit battery models. The simplicity of these models leads to analytic optimal solutions in the linear case, providing insights to guide the setup of the optimization problem for the nonlinear case. Parameter estimation accuracy improves significantly as a result of this optimization. The paper demonstrates this improvement for multiple electrified vehicle configurations.

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