scholarly journals Broadband Impedance Measurement of Lithium-Ion Battery in the Presence of Nonlinear Distortions

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2493
Author(s):  
Jussi Sihvo ◽  
Tomi Roinila ◽  
Daniel-Ioan Stroe
Keyword(s):  
Electrochemical Impedance ◽  
Equivalent Circuit Model ◽  
Impedance Measurement ◽  
Lithium Ion ◽  
Li Ion Battery ◽  
Third Order ◽  
Impedance Measurements ◽  
Nonlinear Distortions ◽  
Measured Impedance ◽  
Li Ion

The impedance of a Lithium-ion (Li-ion) battery has been shown to be a valuable tool in evaluating the battery characteristics such as the state-of-charge (SOC) and state-of-health (SOH). Recent studies have shown impedance-measurement methods based on broadband pseudo-random sequences (PRS) and Fourier techniques. The methods can be efficiently applied in real-time applications where the conventional electrochemical-impedance spectroscopy (EIS) is not well suited to measure the impedance. The techniques based on the PRS are, however, strongly affected by the battery nonlinearities. This paper presents the use of a direct-synthesis ternary (DST) signal to minimize the effect caused by the nonlinearities. In such a signal, the second- and third-order harmonics are suppressed from the signal energy spectrum. As a result, the effect of the second- and third-order nonlinearities are suppressed from the impedance measurements. The impedance measurements are carried out for a nickel manganese cobalt Li-ion battery cell. The performance of the method is compared to the conventional EIS, as well as to other PRS signals which are more prone to battery nonlinearities. The Kronig–Kramers (K–K) transformation test is used to validate the uniqueness of the measured impedance spectra. It is shown that the measurement method based on the DST produces highly accurate impedance measurements under nonlinear distortions of the battery. The method shows a good K–K test behavior indicating that the measured impedance complies well to a linearized equivalent circuit model that can be used for the SOC and SOH estimation of the battery. Due to the good performance, low measurement time, and simplicity of the DST, the method is well suited for practical battery applications.

scholarly journals Guidelines for the Characterization of the Internal Impedance of Lithium-Ion Batteries in PHM Algorithms

2020 ◽  
Vol 9 (3) ◽  
Author(s):  
Aramis Pérez ◽  
Matias Benavides ◽  
Heraldo Rozas ◽  
Sebastián Seria ◽  
Marcos Orchard
Keyword(s):  
Electrochemical Impedance ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Dynamic Parameter ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Internal Impedance ◽  
Battery Degradation ◽  
Li Ion

This article aims to describe the most important aspects to consider when using the concept of internal impedance in algorithms that focus on characterizing the degradation of lithium-ion (Li-ion) batteries. The first part of the article provides a literature review that will help the reader understand the concept of electrochemical impedance spectroscopy (EIS) and how Li-ion batteries can be represented through electrochemical or empirical models, in order to interpret the outcome of typical discharge and/or degradation tests on Li-ion batteries. The second part of the manuscript shows the obtained results of an accelerated degradation experiment performed under controlled conditions on a Li-ion cell. Results show that changes observed on the EIS test can be linked to battery degradation. This knowledge may be of great value when implementing algorithms aimed to predict the End-of-Life (EoL) of the battery in terms of temperature, voltage, and discharge current measurements. The purpose of this article is to introduce the reader to several types of Li-ion battery models, and show how the internal impedance of a Li-ion battery is a dynamic parameter that depends on different factors; and then, illustrate how the EIS can be used to obtain an equivalent circuit model and how the different electronic components vary with the use given to the battery.

scholarly journals Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
An Wen ◽  
Jinhao Meng ◽  
Jichang Peng ◽  
Lei Cai ◽  
Qian Xiao
Keyword(s):  
Instrumental Variable ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Parameters Estimation ◽  
Measurement Noise ◽  
Recursive Least Squares ◽  
Ar Model ◽  
Noise Model ◽  
Li Ion Battery ◽  
Li Ion

Refined Instrumental Variable (RIV) estimation is applied to online identify the parameters of the Equivalent Circuit Model (ECM) for Lithium-ion (Li-ion) battery in this paper, which enables accurate parameters estimation with the measurement noise. Since the traditional Recursive Least Squares (RLS) estimation is extremely sensitive to the noise, the parameters in the ECM may fail to converge to their true values under the measurement noise. The RIV estimation is implemented in a bootstrap form, which alternates between the estimation in the system model and the noise model. The Box-Jenkins model of the Li-ion battery transformed from the two RC ECM is selected as the transfer function model for the RIV estimation in this paper. The errors of the two RC ECM are independently generated by the residual of high-order Auto Regressive (AR) model estimation. With the benefit of a series of auxiliary models, the data filtering technology can prefilter the measurement and increase the robustness of the parameters against the noise. Reasonable parameters are possible to be obtained regardless of the noise in the measurement by RIV. Simulation and experimental tests on a LiFePO4 battery validate the efficiency of RIV for parameter online identification compared with traditional RLS.

Criteria for Reliable Electrochemical Impedance Measurements on Li-Ion Battery Anodes

2003 ◽  
Vol 150 (2) ◽  
pp. A143 ◽  
Author(s):  
Chunsheng Wang ◽  
A. John Appleby ◽  
Frank E. Little
Keyword(s):  
Electrochemical Impedance ◽  
Li Ion Battery ◽  
Impedance Measurements ◽  
Li Ion

State-of-Health Online Estimation for Li-Ion Battery

2020 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Liu Fang ◽  
◽  
Liu Xinyi ◽  
Su Weixing ◽  
Chen Hanning ◽  
...  
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Battery Management ◽  
State Of Health ◽  
Li Ion Battery ◽  
Improved Genetic Algorithm ◽  
Battery Management Systems ◽  
Li Ion

To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.

scholarly journals Comparative Study of Equivalent Circuit Models Performance in Four Common Lithium-Ion Batteries: LFP, NMC, LMO, NCA

Batteries ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Manh-Kien Tran ◽  
Andre DaCosta ◽  
Anosh Mevawalla ◽  
Satyam Panchal ◽  
Michael Fowler
Keyword(s):  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Battery Modeling ◽  
Li Ion ◽  
And Control

Lithium-ion (Li-ion) batteries are an important component of energy storage systems used in various applications such as electric vehicles and portable electronics. There are many chemistries of Li-ion battery, but LFP, NMC, LMO, and NCA are four commonly used types. In order for the battery applications to operate safely and effectively, battery modeling is very important. The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control Li-ion batteries. In this study, experiments were performed to investigate the performance of three different ECMs (1RC, 2RC, and 1RC with hysteresis) on four Li-ion battery chemistries (LFP, NMC, LMO, and NCA). The results indicated that all three models are usable for the four types of Li-ion chemistries, with low errors. It was also found that the ECMs tend to perform better in dynamic current profiles compared to non-dynamic ones. Overall, the best-performed model for LFP and NCA was the 1RC with hysteresis ECM, while the most suited model for NMC and LMO was the 1RC ECM. The results from this study showed that different ECMs would be suited for different Li-ion battery chemistries, which should be an important factor to be considered in real-world battery and BMS applications.

An Ensemble Bias-Correction Method With Adaptive Weights for Dynamic Modeling of Lithium-Ion Batteries

2017 ◽  
Author(s):  
Yifei Li ◽  
Mohammad Kazem Sadoughi ◽  
Zhixiong Li ◽  
Chao Hu
Keyword(s):  
Dynamic Modeling ◽  
Dynamic Characteristics ◽  
Bias Correction ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Correction Method ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Adaptive Weights ◽  
Li Ion

Accurate modeling of the electrical behavior of a lithiumion (Li-ion) battery can provide accurate dynamic characteristics of the battery during charging/discharging and relaxation phases, which is essential to accurate online estimation of the battery state of charge (SoC). This paper proposes an ensemble bias-correction (BC) method with adaptive weights to improve the accuracy of an equivalent circuit model (ECM) in dynamic modeling of Li-ion batteries. The contribution of this paper is twofold: (i) the development of a novel ensemble method based on BC learning to model the dynamic characteristics of Li-ion batteries; and (ii) the creation of an adaptive-weighting scheme to learn online the weights of offline member BC models for building an online ensemble BC model. Repeated pulsing tests with single and multiple C-rates were conducted on seven Li-ion battery cells to evaluate the effectiveness of the proposed ensemble BC method. The analysis results with the use of an ECM demonstrate that the proposed method can reduce, on average, the voltage modeling error of the ECM by at least 50%.

scholarly journals Fast Impedance Measurement of Li-Ion Battery Using Discrete Random Binary Excitation and Wavelet Transform

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 46152-46165
Author(s):  
Gjorgji Nusev ◽  
Dani Juricic ◽  
Miran Gaberscek ◽  
Joze Moskon ◽  
Pavle Boskoski
Keyword(s):  
Wavelet Transform ◽  
Impedance Measurement ◽  
Li Ion Battery ◽  
Li Ion

Enhancing Co/Co2VO4 Li-ion Battery Anode Performances via 2D-2D Heterostructure Engineering

Nanoscale ◽  
2021 ◽  
Author(s):  
Kun Wang ◽  
Yongyuan Hu ◽  
Jian Pei ◽  
Fengyang Jing ◽  
Zhongzheng Qin ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Anode Material ◽  
Output Voltage ◽  
High Capacity ◽  
Lithium Ion ◽  
Li Ion Battery ◽  
Li Ion ◽  
Battery Anode

High capacity Co2VO4 becomes a potential anode material for lithium ion batteries (LIBs) benefiting from its lower output voltage during cycling than other cobalt vanadates. However, the application of this...

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