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 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.

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.

scholarly journals Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1074 ◽  
Author(s):  
Yu Miao ◽  
Patrick Hynan ◽  
Annette von Jouanne ◽  
Alexandre Yokochi
Keyword(s):  
Electric Vehicles ◽  
Electrode Materials ◽  
Negative Electrode ◽  
Lithium Ion ◽  
High Energy ◽  
High Energy Density ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Li Ion ◽  
On The Road

Over the past several decades, the number of electric vehicles (EVs) has continued to increase. Projections estimate that worldwide, more than 125 million EVs will be on the road by 2030. At the heart of these advanced vehicles is the lithium-ion (Li-ion) battery which provides the required energy storage. This paper presents and compares key components of Li-ion batteries and describes associated battery management systems, as well as approaches to improve the overall battery efficiency, capacity, and lifespan. Material and thermal characteristics are identified as critical to battery performance. The positive and negative electrode materials, electrolytes and the physical implementation of Li-ion batteries are discussed. In addition, current research on novel high energy density batteries is presented, as well as opportunities to repurpose and recycle the batteries.

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 A Look at Some International Lithium Ion Battery Recycling Initiatives

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
A. Mancha
Keyword(s):  
Public Health ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
The United States ◽  
Portable Devices ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Material Supply ◽  
Battery Recycling ◽  
Li Ion

Today the United States is heavily reliant on the lithium-ion battery as most portable devices and electronics run on it. Current innovations are also looking on how to maximize it on the grid and transportation. This paper will look at three sovereign states and their current initiatives on Li-ion battery recycling: US, European Union, and China. The term initiative is used loosely as the information is not permanent in most policies or plans. Li-ion battery recycling initiatives are crucial to look at because used and wasted Li-ion batteries can disrupt public health and Li-ion batteries are expected to be a factor for effective material supply for future battery production especially in transportation, like the Tesla Roadster.

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 Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries

Processes ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 38 ◽  
Author(s):  
Jeongeun Son ◽  
Yuncheng Du
Keyword(s):  
Fault Detection ◽  
Lithium Ion ◽  
Fault Detection And Diagnosis ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Probabilistic Description ◽  
Li Ion ◽  
Detection And Diagnosis ◽  
Physical Phenomena ◽  
Battery Cells

The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may consequently introduce a mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct a model of Li-ion battery cells and to identify faulty operations in a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between the measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm is shown to be efficient in terms of the fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations.

Modeling of Multi-Cell Lithium-Ion Battery Packs for Electric Vehicles Considering Effects of Manufacturing Processes

2013 ◽  
Author(s):  
Chongye Wang ◽  
Yong Wang ◽  
Lin Li ◽  
Hua Shao ◽  
Changxu Wu
Keyword(s):  
Carbon Dioxide Emissions ◽  
Single Cells ◽  
Chemical Properties ◽  
Lithium Ion ◽  
Manufacturing Processes ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion ◽  
Multiple Cells

Electric vehicle (EV) technologies have received great attention due to the potential contributions to relieving the energy dependence on petroleum and reducing carbon dioxide emissions. The advancement of EV technologies greatly relies on the development of battery technologies. Lithium-ion (Li-ion) batteries have recently become the main choice as the power source for major EV manufacturers. Previous research on EV Li-ion batteries is mainly focused on materials and chemical properties of single cells, while the effects of manufacturing processes on the performance of entire battery packs have almost been neglected. In practice, EV batteries are used in packs containing multiple cells, which may not be ideally manufactured. This research proposes a novel modeling method for analyzing the effects of manufacturing processes on the dynamics of EV Li-ion battery packs. The method will help engineers gain a deeper understanding of the roles of manufacturing processes in improving EV Li-ion battery performance.

Energy Conservation and Management Strategies for Commercial Li-Ion Batteries in Telecommunication Applications

2010 ◽  
Vol 72 ◽  
pp. 325-330
Author(s):  
Tomonobu Tsujikawa ◽  
K. Yabuta ◽  
T. Matsushita ◽  
M. Arakawa ◽  
K. Hayashi
Keyword(s):  
Management Strategies ◽  
Lithium Ion ◽  
Discharge Voltage ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Electrolytic Solution ◽  
Battery Lifetime ◽  
Li Ion ◽  
Short Circuits ◽  
Storage Batteries

In addition to cost and lifetime, important factors in using lithium-ion (Li-ion) batteries as a backup power supply in telecommunication applications are safety and the flatness of discharge voltage to maintain compatibility with existing systems based on lead storage batteries. We have been researching Li-ion batteries using manganese spinel as cathode material from the viewpoints of flat discharge voltage and thermal stability in the event of overcharging or internal short-circuits. The safety of the Li-ion battery was improved by adding a phosphazene flame retardant to the electrolytic solution and cathode of the battery. Then, with the aim of extending battery lifetime, some of the manganese in the cathode was replaced by another metallic element, which showed favorable results.

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