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.

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 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 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 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.

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 Empirical and physics-based approaches to estimate states of lithium-ion battery

2019 ◽  
Vol 21 (2) ◽  
pp. 259-268
Author(s):  
Anna A. Fedorova
Keyword(s):  
Embedded System ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Market Demand ◽  
Huge Number ◽  
Battery Model ◽  
The Embedded System

Lithium-ion batteries are integral parts of our life due to the rapid increase of applications which require batteries for their exploitation. Thus, there is a market demand to produce lithium-ion batteries for a huge number of applications from electric vehicles to energy storages. Battery Management System (BMS) is developed to maintain safe battery exploitation conditions. Most BMSs are embedded systems that have physical memory limits. Therefore, battery model should be easy to simulate to be integrated into BMS for states estimation. In the present paper we intend to compare empirical and physics-based approaches to estimate lithium-ion battery states with respect to their possibility of implementation in the embedded system. We will use Kalman filter to estimate battery states by means of the mentioned models.

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