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

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.

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.

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

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.

scholarly journals Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles

2019 ◽  
Vol 9 (6) ◽  
pp. 4876-4882
Author(s):  
Y. Muratoglu ◽  
A. Alkaya
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Balance Control ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Battery Model ◽  
State Of Charge Estimation

Accurate state of charge estimation and robust cell equalization are vital in optimizing the battery management system and improving energy management in electric vehicles. In this paper, the passive balance control based equalization scheme is proposed using a combined dynamic battery model and the unscented Kalman filter based state of charge estimation. The lithium-ion battery is modeled with a 2nd order Thevenin equivalent circuit. The combined dynamic model of the lithium-ion battery, where the model parameters are estimated depending on the state of charge, and the unscented Kalman filter based state of charge, are used to improve the performance of the passive balance control based equalization. The experimental results verified the superiority of the combined dynamic battery model and the unscented Kalman filter algorithm with very tight error bounds. Furthermore, these results showed that the presented passive balance control based equalization scheme is suitable for the equalization of series-connected lithium-ion batteries.

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