scholarly journals Structural Effects of Anomalous Current Densities on Manganese Hexacyanoferrate for Li-Ion Batteries

2020 ◽  
Vol 10 (21) ◽  
pp. 7573
Author(s):  
Angelo Mullaliu ◽  
Stéphanie Belin ◽  
Lorenzo Stievano ◽  
Marco Giorgetti ◽  
Stefano Passerini
Keyword(s):  
Coulombic Efficiency ◽  
Lithium Ion ◽  
Electrochemical Process ◽  
Remaining Useful Life ◽  
Multivariate Curve Resolution ◽  
Battery Management System ◽  
Battery Management ◽  
Structural Effects ◽  
Current Densities ◽  
Li Ion

A battery management system (BMS) plays a pivotal role in providing optimal performance of lithium-ion batteries (LIBs). However, the eventual malfunction of the BMS may lead to safety hazards or reduce the remaining useful life of LIBs. Manganese hexacyanoferrate (MnHCF) was employed as the positive electrode material in a Li-ion half-cell and subjected to five cycles at high current densities (10 A gMnHCF−1) and to discharge at 0.1 A gMnHCF−1, instead of classical charge/discharge cycling with initial positive polarization at 0.01 A gMnHCF−1, to simulate a current sensor malfunctioning and to evaluate the electrochemical and structural effects on MnHCF. The operando set of spectra at the Mn and Fe K-edges was further analyzed through multivariate curve resolution analysis with an alternating least squares algorithm (MCR–ALS) and extended X-ray absorption fine structure (EXAFS) spectroscopy to investigate the structural modifications arising during cycling after the applied electrochemical protocol. The coulombic efficiency in the first cycle was dramatically affected; however, the local structural environment around each photo absorber recovered during charging. The identification of an additional spectral contribution in the electrochemical process was achieved through MCR-ALS analysis, and the Mn-local asymmetry was thoroughly explored via EXAFS analysis.

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.

A Piecewise Linear Time-Varying Model for Modeling the Charging and Discharging Processes of a Lithium-Ion Battery

2014 ◽  
Vol 5 (2) ◽  
pp. 87-103
Author(s):  
Arab AlSharif ◽  
Manohar Das
Keyword(s):  
Test Data ◽  
Linear Time ◽  
Piecewise Linear ◽  
Lithium Ion ◽  
Battery Management System ◽  
Time Varying ◽  
Battery Management ◽  
Simulated Test ◽  
Li Ion ◽  
Charging And Discharging Processes

A piecewise linear, time-varying model for modeling the charging and discharging processes of Li-ion batteries is introduced in this paper. Such a model consists of a group of piecewise linear model segments, whose parameters are adapted online over time. Thus, the combined overall model is capable of modeling nonlinear time-varying processes, such as a Li-ion battery charging and discharging processes, quite well. Modeling results of both simulated test data and actual test data gathered from a high-power automotive-grade Li-ion cell are presented. The close matches between actual and model-predicted behaviors demonstrate the effectiveness of the proposed modeling approach and indicate the potential usefulness of such models for a battery management system.

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 A Novel Battery Management System for Series Parallel Connected Li-Ion Battery Pack for Electric Vehicle Application.

2019 ◽  
Vol 8 (4) ◽  
pp. 496-499
Keyword(s):  
Management System ◽  
Lithium Ion ◽  
Green Energy ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Efficient Operation ◽  
In Series ◽  
Li Ion

The green energy evolution initiated the use of electric and hybrid electric vehicles at present on roads. These vehicles extensively use different types of batteries and among them lithium ion batteries are prominent. The Li-ion battery pack constitutes number of Li-ion battery cells connected in series and parallel configuration. This battery bank needs a suitable battery management system for its efficient operation. This paper presents a novel battery management system to monitor and control the battery current, voltage, state of charge and most importantly the cell temperature. The detail BMS scheme for Li-ion battery pack is presented and simulation is carried out to validate its performance with a driving cycle of electric car.

DESIGN OF BATTERY MANAGEMENT SYSTEM FOR ELECTRIC VEHICLE BATTERY-BASED HYBRID METAL-ORGANIC (SOL-GEL) LITHIUM MANGANATE (LiMn2O4)

2016 ◽  
Vol 6 (1) ◽  
pp. 19
Author(s):  
Wisnu Ananda ◽  
Mehammed Nomeri
Keyword(s):  
Electric Vehicle ◽  
Management System ◽  
Sol Gel ◽  
Lithium Ion ◽  
Support Vector ◽  
Battery Management System ◽  
Battery Management ◽  
Electric Cars ◽  
Electric Vehicle Battery ◽  
Li Ion

Battery-powered Electric Vehicles (BEVs) such as electric cars, use the battery as the main power source to drive the motor, in addition to lighting, horn, and other functions. Currently, Balai Besar Bahan dan Barang Teknik (B4T) has been conducting research in Lithium-ion (Li-ion) battery prototype for an electric vehicle. However, the management system in accordance with the electrical characteristics of the battery prototype is still not available. Thus, to integrate the battery prototype with electrical components of the electric vehicle, it is necessary to design Battery Management System (BMS). Two important battery parameters observed are State of Charge (SOC) and State  of  Health  (SOH).  The  method  used  for  SOC  was  Coulomb  Counting.  SOH  was  determined  using  a combination between Support Vector Machine (SVM) and Relevance Vector Machine (RVM). Based on the experiments by using BMS, the battery performance could be more controlled and produces a linear curve of SOC and SOH.Keywords: Battery, electric vehicle, Battery Management System (BMS), Lithium-ion (Li-ion).

Comparison of Prognostic Algorithms Based on PF and EKF for Estimating Remaining Useful Life of Li-Ion Batteries

2014 ◽  
Vol 529 ◽  
pp. 616-620
Author(s):  
Jin Zhang ◽  
An Tong Gao ◽  
Wen Bing Wang ◽  
Yu Sheng Han ◽  
Rong Gang Chen ◽  
...  
Keyword(s):  
Remaining Useful Life ◽  
Lumped Parameter Model ◽  
Temperature Cycle ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Lumped Parameter ◽  
Remaining Capacity ◽  
Li Ion ◽  
Useful Life

To estimate remaining useful life (RUL) of Li-ion batteries is a key factor for correct and safe battery management, particularly for the development of Battery Management System (BMS). A lumped parameter model which integrates the non-linear open-circuit voltage, current, temperature, cycle number, and remaining capacity and other dynamic characteristics is created based on the battery electrical characteristics is presented. A particle filter (PF) algorithm which syncretizes Li-ion battery electrochemical working process is proposed according to the sequence importance of re-sampling to predict its discharge end time in single cycle time and cycle life. Besides, for comparison, a extended Kalman filter (EKF) algorithm is also proposed to estimate the RUL based on the same statistics. Simulation results show that the PF algorithm according to lumped parameter model has a better precision in estimating RUL compared with the EKF algorithm.

scholarly journals Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 446 ◽  
Author(s):  
Muhammad Umair Ali ◽  
Amad Zafar ◽  
Sarvar Hussain Nengroo ◽  
Sadam Hussain ◽  
Muhammad Junaid Alvi ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Management System ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

scholarly journals Classifying Aged Li-Ion Cells from Notebook Batteries

2020 ◽  
Vol 12 (9) ◽  
pp. 3620 ◽  
Author(s):  
Felipe Salinas ◽  
Julia Kowal
Keyword(s):  
Lithium Ion ◽  
Battery Management System ◽  
Battery Management ◽  
Life Conditions ◽  
Lithium Ion Cells ◽  
Capacity Degradation ◽  
Boosted Decision Tree ◽  
Li Ion ◽  
Aging Mechanisms ◽  
Charge Discharge

A dataset consisting of 90 lithium-ion cells obtained from old notebook batteries containing their response to 100 charge–discharge cycles is presented. The resulting degradation patterns are assigned to four clusters and related to possible aging mechanisms. The records in the battery management system (BMS) of each battery are analyzed to understand the influence of first life conditions in the measured degradation patterns. The analysis reveals that a cluster of cells which experienced mostly calendar aging in 7–13 years hold ~90% of the rated capacity, and exhibit at 0.4 C discharge a linear capacity degradation throughout cycling comparable to new cells. In contrast, a cluster of cells that experienced extensive calendar and cyclic aging can lose ~50% capacity at 0.4 C discharge in a few cycles after reutilization. A model based on a boosted decision tree is applied to forecast the cluster of each cell, using as features the capacity measured in the first cycle, and the records obtained from the BMS. The highest accuracy (83%) is obtained through capacity, where misclassification arises from two clusters containing highly degraded cells with similar initial capacities, but divergent degradation patterns.

scholarly journals Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1309
Author(s):  
Akash Samanta ◽  
Sumana Chowdhuri ◽  
Sheldon S. Williamson
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Battery Management System ◽  
Battery Management ◽  
Knowledge Based ◽  
Future Challenges

Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

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