A PDE-Based Approach for Fault Detection in Li-Ion Batteries

2017 ◽  
Author(s):  
Hasan Ferdowsi ◽  
Nima Lotfi
Keyword(s):  
Fault Detection ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Model Approximation ◽  
Detection Scheme ◽  
Portable Electronics ◽  
Safety And Reliability ◽  
Li Ion ◽  
Occurrence Type ◽  
Abrupt Faults

Despite the widespread commercialization of Li-ion batteries in various markets including portable electronics, electrified transportation, and stationary energy storage systems, their safety and reliability still poses as a concern in the eyes of industry and general public. There has been great strides in the past few decades in the development of Battery Management Systems (BMSs). The majority of the efforts, however, avoid fault occurrence by conservative designs rather than directly incorporating fault diagnostics in the BMS. Such a functionality in the BMS would enable the detection of the occurrence, type, and location of the faults and therefore, a proper reaction to them. Realizing the need for such a feature in the BMSs, the development of a model-based fault detection scheme is proposed in this paper. This method is formulated based on the original PDEs describing a single particle electrochemical battery model. The use of PDEs in the fault detection scheme reduces uncertainties arising from the model approximation. Furthermore, the convergence of this PDE-based approach is proved using Lyapunov stability theorem. Finally, the effectiveness of the proposed method in detecting various fault types ranging from incipient degradation mechanisms to abrupt faults is illustrated through simulations.

scholarly journals Designing of Lithium - Ion Battery Pack Rechargeable on a Hybrid System with Battery Management System (BMS) for DC Loads of Low Power Applications – A Prototype Model

2021 ◽  
Vol 2089 (1) ◽  
pp. 012017
Author(s):  
Ramu Bhukya ◽  
Praveen Kumar Nalli ◽  
Kalyan Sagar Kadali ◽  
Mahendra Chand Bade
Keyword(s):  
Lithium Ion Battery ◽  
Management System ◽  
Short Circuit ◽  
Lithium Ion ◽  
Battery Pack ◽  
Prototype Model ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion

Abstract Now a days, Li-ion batteries are quite possibly the most exceptional battery-powered batteries; these are drawing in much consideration from recent many years. M Whittingham first proposed lithium-ion battery technology in the 1970s, using titanium sulphide for the cathode and lithium metal for the anode. Li-ion batteries are the force to be reckoned with for the advanced electronic upset in this cutting-edge versatile society, solely utilized in cell phones and PC computers. A battery is a Pack of cells organized in an arrangement/equal association so the voltage can be raised to the craving levels. Lithium-ion batteries, which are completely utilised in portable gadgets & electric vehicles, are the driving force behind the digital technological revolution in today’s mobile societies. In order to protect and maintain voltage and current of the battery with in safe limit Battery Management System (BMS) should be used. BMS provides thermal management to the battery, safeguarding it against over and under temperature and also during short circuit conditions. The battery pack is designed with series and parallel connected cells of 3.7v to produce 12v. The charging and releasing levels of the battery pack is indicated by interfacing the Arduino microcontroller. The entire equipment is placed in a fiber glass case (looks like aquarium) in order to protect the battery from external hazards to design an efficient Lithium-ion battery by using Battery Management System (BMS). We give the supply to the battery from solar panel and in the absence of this, from a regular AC supply.

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 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.

Implementation of the Li-Ion Battery Management System Based on DS2438

2015 ◽  
Vol 733 ◽  
pp. 714-717 ◽  
Author(s):  
Ping Yang ◽  
Hou Yu Yu ◽  
Yong Gang Yan
Keyword(s):  
Management System ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Electric Cars ◽  
Power Battery ◽  
Electric Vehicle Battery ◽  
Li Ion

In order to ensure good performance and extend the lifetime of li-ion batteries in electric cars, effective real-time monitoring and management must be valued. This paper designs an electric vehicle battery management system based on a smart battery monitoring chip, DS2438. It integrates the measurement of battery's temperature, voltage, current, and power as a whole, which not only simplifies the circuit, but also saves system cost. The battery’s SOC (State Of Charge) can be easily estimated and displayed in this design. It improves the reliability of power battery pack and prolonged its life, which can be used as reference to battery management system design and application.

scholarly journals Investigation of the Physico-Chemical Properties of the Products Obtained after Mixed Organic-Inorganic Leaching of Spent Li-Ion Batteries

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6732
Author(s):  
Weronika Urbańska ◽  
Magdalena Osial
Keyword(s):  
Hydrogen Peroxide ◽  
Glutaric Acid ◽  
Chemical Properties ◽  
Lithium Ion ◽  
Li Ion Batteries ◽  
X Ray Diffraction ◽  
Widespread Application ◽  
Portable Electronics ◽  
Physico Chemical ◽  
Li Ion

Lithium-ion batteries are currently one of the most important mobile energy storage units for portable electronics such as laptops, tablets, smartphones, etc. Their widespread application leads to the generation of large amounts of waste, so their recycling plays an important role in environmental policy. In this work, the process of leaching with sulfuric acid for the recovery of metals from spent Li-ion batteries in the presence of glutaric acid and hydrogen peroxide as reducing agents is presented. Experimental results indicate that glutaric-acid application improves the leaching performance compared to the use of just hydrogen peroxide under the same conditions. Obtained samples of leaching residues after mixed inorganic-organic leaching were characterized with Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy, and X-ray diffraction.

A Diagnostic Scheme for Detection, Isolation and Estimation of Electrochemical Faults in Lithium-Ion Cells

2015 ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Sliding Mode ◽  
Direct Method ◽  
Lithium Ion ◽  
Battery Management ◽  
Li Ion Battery ◽  
Modeling Framework ◽  
Battery Cell ◽  
Safety And Reliability ◽  
Li Ion ◽  
Sliding Mode Observers

Improvement of the safety and reliability of the Lithium-ion (Li-ion) battery operation is one of the key tasks for advanced Battery Management Systems (BMSs). It is critical for BMSs to be able to diagnose battery electrochemical faults that can potentially lead to catastrophic failures. In this paper, an observer-based fault diagnosis scheme is presented that can detect, isolate and estimate some internal electrochemical faults. The scheme uses a reduced-order electrochemical-thermal model for a Li-ion battery cell. The paper first presents a modeling framework where the electrochemical faults are modeled as parametric faults. Then, multiple sliding mode observers are incorporated in the diagnostic scheme. The design and selection of the observer gains as well as the convergence of the observers are verified theoretically via Lyapunov’s direct method. Finally, the performance of the observer-based diagnostic scheme is illustrated via simulation studies.

Magnetography: A novel Characterization Tool for Li-Ion-Batteries

2013 ◽  
Vol 1544 ◽  
Author(s):  
Timm Bergholz ◽  
Theodor Nuñez ◽  
Jürgen Wackerl ◽  
Carsten Korte ◽  
Detlef Stolten
Keyword(s):  
Magnetic Field ◽  
Normal Operation ◽  
Additional Parameter ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Non Invasive ◽  
Spatially Resolved ◽  
Standardized Measurement ◽  
Li Ion ◽  
The Magnetic Field

ABSTRACTThe application of magnetography as a novel method to determine the state of charge (SoC) of commercial Li-ion Batteries is reported. The method is non-invasive and nondestructive and suitable to be applied during normal operation. It is based on spatially resolved measurement of the magnetic field B, induced by the changing current flow during cycling. A standardized measurement setup and procedure for conventional AMR-sensors has been developed, offering high reproducibility (∼0.1%) and the chance to characterize the different spatial components of the magnetic field (Bx, By, Bz). The percentage deviation of the B-distributions for different SoCs for a given current load reveals significant differences. A change of B of up to 20% between SoCs of 90% and 10% is found. The influence of current density at different SoC reveals a constant magnetic susceptibility χ at low SoC and a field dependent χ at high SoC. Both effects are attributed to the change of the magnetic properties upon varying the amount of intercalated lithium in the transition metal (LixNi1/3Co1/3Mn1/3O2) based intercalation cathode. The method can be used to provide an additional parameter for SoCestimation to battery management systems.

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