Control-Oriented Modeling of Lithium-Ion Batteries

2020 ◽  
Author(s):  
Brody Riemann ◽  
Jie Li ◽  
Kasim Adewuyi ◽  
Robert Landers ◽  
Jonghyun Park
Keyword(s):  
Low Frequency ◽  
Lithium Ion ◽  
Battery Management ◽  
Computationally Efficient ◽  
Low Frequencies ◽  
Battery Modeling ◽  
Capacitance Effect ◽  
Frequency Domains ◽  
Frequency Behavior ◽  
Time And Frequency Domains

Abstract Battery Management Systems (BMSs) require control-oriented models. Physics-based electrochemical models describe detailed battery phenomena, but are too computationally intensive for use in BMSs. Single Particle Models (SPMs) are often used for control-oriented battery modeling since they are physics-based and computationally efficient; however, they are only valid over very low frequency ranges and C-rates. Empirical Equivalent Circuit Models (ECMs) are also used in BMSs since they are computationally efficient and describe battery behavior over wide frequency ranges; however, they provide no physical understanding of the battery and often employ fractional order terms. This work provides a control-oriented battery model that combines the benefits of SPM and ECM models, while overcoming their limitations. The proposed model incorporates some of the battery physics found in electrochemical models, can easily be used in both the time and frequency domains, and describes battery behavior over its entire frequency range. A linearized SPM models battery physics at very low frequencies. For low frequencies, integer-order linear systems are used to approximate diffusion physics, and high frequency behavior is modeled by the double layer capacitance effect. The model is validated in the time and frequency domains via a comparison to Pseudo 2-Dimensional (P2D) model simulations and experimental data.

scholarly journals Determination of the Distribution of Relaxation Times by Means of Pulse Evaluation for Offline and Online Diagnosis of Lithium-Ion Batteries

Batteries ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 36
Author(s):  
Erik Goldammer ◽  
Julia Kowal
Keyword(s):  
Lithium Ion Batteries ◽  
Electrochemical Impedance ◽  
Sampling Rate ◽  
Relaxation Times ◽  
Low Frequency ◽  
Lithium Ion ◽  
Battery Management ◽  
Time Constants ◽  
Time Domain Data ◽  
Aging Study

The distribution of relaxation times (DRT) analysis of impedance spectra is a proven method to determine the number of occurring polarization processes in lithium-ion batteries (LIBs), their polarization contributions and characteristic time constants. Direct measurement of a spectrum by means of electrochemical impedance spectroscopy (EIS), however, suffers from a high expenditure of time for low-frequency impedances and a lack of general availability in most online applications. In this study, a method is presented to derive the DRT by evaluating the relaxation voltage after a current pulse. The method was experimentally validated using both EIS and the proposed pulse evaluation to determine the DRT of automotive pouch-cells and an aging study was carried out. The DRT derived from time domain data provided improved resolution of processes with large time constants and therefore enabled changes in low-frequency impedance and the correlated degradation mechanisms to be identified. One of the polarization contributions identified could be determined as an indicator for the potential risk of plating. The novel, general approach for batteries was tested with a sampling rate of 10 Hz and only requires relaxation periods. Therefore, the method is applicable in battery management systems and contributes to improving the reliability and safety of LIBs.

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 An Efficient Electrochemical Tanks-in-Series Model for Lithium-Sulfur Batteries

2020 ◽  
Author(s):  
Caitlin D. Parke ◽  
Akshay Subramaniam ◽  
Suryanarayana Kolluri ◽  
Daniel T. Schwartz ◽  
Venkat R. Subramanian
Keyword(s):  
Lithium Ion ◽  
Battery Management ◽  
Computationally Efficient ◽  
Tank Model ◽  
Wide Range ◽  
Lithium Sulfur Batteries ◽  
In Series ◽  
Model Equations ◽  
Electrochemical Model ◽  
Lithium Sulfur

This article applies and efficiently implements the Tanks-in-Series methodology (J. Electrochem. Soc., 167, 013534 (2020)) to generate a computationally efficient electrochemical model for Lithium Sulfur batteries. The original Tank model approach for Lithium-ion batteries is modified to account for porosity changes with time. In addition, exponential scaling is introduced that enables efficient simulation of the model equations to address a wide range of time constants present for different reactions in Lithium-sulfur system. The tank model achieves acceptable voltage error even for relatively aggressive conditions of discharge. Predictions of internal electrochemical variables are examined, and electrochemical implications of the approximations discussed. This suggests significant potential for real-time applications such as optimal charging, cell-balancing and estimation, and represents a step forward in efforts to incorporate detailed electrochemical models in advanced Battery Management Systems for Lithium-Sulfur batteries.

Lithium Ion Dynamic Battery Pack Model and Simulation for Automotive Applications

2009 ◽  
Author(s):  
Benjamin J. Yurkovich ◽  
Yann Guezennec
Keyword(s):  
Lithium Ion ◽  
Battery Pack ◽  
Parameter Distribution ◽  
Battery Management System ◽  
Simulation Tool ◽  
Battery Management ◽  
Automotive Applications ◽  
Computationally Efficient ◽  
Model And Simulation ◽  
Battery Packs

In this paper, we introduce a lumped parameter, distributed battery pack dynamic model which allows simulation of the electrical dynamics of all the cells in an arbitrarily configured series/parallel pack typical of those used in automotive applications. The dynamic pack simulator is based on the development of an analytical solution for the dynamic response of a single cell and an analytical development of such elemental solutions into a distributed dynamic pack model which can resolve the dynamics of each cell within the pack. This formulation leads to a computationally efficient simulation tool appropriate for application on large battery packs. This simulation tool is then used to perform Monte Carlo simulations on typical automotive current profiles for packs made of cells with a statistical distribution of parameters. A mild distribution of cell mismatch leads to cell unbalance development and statistical metrics for the growth unbalance, presented and related to both current severity and cell parameter distribution. The tool is ideally suited for studies in Battery Management System (BMS) algorithm development, as well as model-based fault propagation and diagnostics.

Low Frequency Noise Behavior in Semiconductors

1997 ◽  
Vol 11 (20) ◽  
pp. 899-907
Author(s):  
S. V. Melkonyan ◽  
F. V. Gasparyan ◽  
V. M. Aroutiunyan
Keyword(s):  
Spectral Density ◽  
Characteristic Frequency ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Low Frequencies ◽  
Frequency Behavior

The low frequency behavior of the generation-recombination noise in the homogeneous semiconductors is investigated. The form of Lorentz law for spectral density of noise at low frequencies is made more precise. It is shown that at superlow frequencies the spectrum of generation-recombination noise changes into the 1/f-law. The characteristic frequency of this change depends on the temperature and dimensions of the sample.

scholarly journals Characteristics of Battery Management Systems of Electric Vehicles with Consideration of the Active and Passive Cell Balancing Process

2021 ◽  
Vol 12 (3) ◽  
pp. 120
Author(s):  
Muhammad Uzair ◽  
Ghulam Abbas ◽  
Saleh Hosain
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Comprehensive Overview ◽  
Battery Modeling ◽  
Vehicle Performance ◽  
Efficient Operation ◽  
Battery Management Systems ◽  
Cell Balancing

Energy shortage and environmental pollution issues can be reduced considerably with the development and usage of electric vehicles (EVs). However, electric vehicle performance and battery lifespan depend on a suitable battery arrangement to meet the various battery performance demands. The safety, reliability, and efficiency of EVs largely depends on the constant monitoring of the batteries and management of battery packs. This work comprehensively reviews different aspects of battery management systems (BMS), i.e., architecture, functions, requirements, topologies, fundamentals of battery modeling, different battery models, issues/challenges, recommendations, and active and passive cell balancing approaches, etc., as compared to the existing works which normally discuss one or two aspects only. The work describes BMS functions, battery models and their comparisons in detail for an efficient operation of the battery pack. Similarly, the work presents a comprehensive overview of issues and challenges faced by BMS and also provides recommendations to address these challenges. Cell balancing is very important for the battery performance and in this work various cell balancing methodologies and their comparisons are also presented in detail. Modeling of a cell balancer is presented and a comparative study is also carried out for active and passive cell balance technique in MATLAB/Simulink with an eight cell battery packcell balancing approach. The result shows that the active cell balancing technique is more advantageous than passive balancing for electrical vehicles using lithium-ion batteries.

scholarly journals Practical Applications of Diffusive Realization of Fractional Integrator with SoftFrac

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1767
Author(s):  
Jerzy Baranowski ◽  
Waldemar Bauer ◽  
Rafał Mularczyk
Keyword(s):  
Fractional Calculus ◽  
Fractional Integral ◽  
Low Frequency ◽  
Phase Lag ◽  
Low Frequencies ◽  
Practical Applications ◽  
Fractional Integrator ◽  
The World ◽  
Frequency Behavior ◽  
Domain Integration

Fractional calculus has found multiple applications around the world. It is especially prevalent in the domains of control and electronics. One of the key elements of fractional applications is the fractional integral (or integrator) which is a backbone of famous PIλD controller. It gives advantages of traditional PID with a limited phase lag. The are, however, issues with implementation, which will allow good low-frequency behavior. In this paper, we consider a diffusive realization of a fractional integrator with the use of quadratures. We implemented this method in numerical package SoftFrac, and we illustrate how different quadratures work for this purpose. We show superiority of bounded domain integration with logarithmic transformation and explain issues with behavior for extremely low frequencies.

Recovery of the acoustic impedance from reflection seismograms

Geophysics ◽  
1983 ◽  
Vol 48 (10) ◽  
pp. 1318-1337 ◽  
Author(s):  
D. W. Oldenburg ◽  
T. Scheuer ◽  
S. Levy
Keyword(s):  
Acoustic Impedance ◽  
Recursion Formula ◽  
Low Frequency ◽  
Normal Incidence ◽  
Computationally Efficient ◽  
Low Frequencies ◽  
Frequency Information ◽  
Reflectivity Function ◽  
Band Limited ◽  
Common Signal

This paper examines the problem of recovering the acoustic impedance from a band‐limited normal incidence reflection seismogram. The convolutional model for the seismogram is adopted at the outset, and it is therefore required that initial processing has removed multiples and recovered true amplitudes as well as possible. In the first portion of the paper we investigate the effect of substituting the deconvolved seismic trace (that is, the band‐limited version of the reflectivity function) into the standard recursion formula for the acoustic impedance. The formalism of linear inverse theory is used to show that the logarithm of the normalized acoustic impedance estimated from the deconvolved seismogram is approximately an average of the true logarithm of the impedance. Moreover, the averaging function is identical to that used in deconvolving the initial seismogram. The advantage of these averages is that they are unique; their disadvantage is that low‐frequency information, which is crucial to making a geologic interpretation, is missing. We next present two methods by which the missing low‐frequency information can be recovered. The first method is a linear programming (LP) construction algorithm which attempts to find a reflectivity function made of isolated delta functions. This method is computationally efficient and robust in the presence of noise. Importantly, it also lends itself to the incorporation of impedance constraints if such geologic information is available. A second construction method makes use of the fact that the Fourier transform of a reflectivity function for a layered earth can be modeled as an autoregressive (AR) process. The missing high and low frequencies can thus be predicted from the band‐limited reflectivity function by standard techniques. Stability in the presence of additive noise on the seismogram is achieved by predicting frequencies outside the known frequency band with operators of different orders and extracting a common signal from the results. Our construction algorithms are shown to operate successfully on a variety of synthetic examples. Two sections of field data are inverted, and in both the results from the LP and AR methods are similar and compare favorably to acoustic impedance features observed at nearby wells.

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