Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning

2015 ◽  
Author(s):  
Chao Hu ◽  
Gaurav Jain ◽  
Craig Schmidt ◽  
Carrie Strief ◽  
Melani Sullivan
Keyword(s):  
Medical Device ◽  
Bayesian Learning ◽  
Lithium Ion ◽  
Scale Model ◽  
Sparse Bayesian Learning ◽  
Li Ion Battery ◽  
Implantable Medical Device ◽  
Charge Voltage ◽  
Battery Capacity ◽  
Li Ion

Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the capacity of the battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the capacity of a Li-ion battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the battery capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online capacity estimation computationally efficient. 10 years’ continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Sheng Shen ◽  
M. K. Sadoughi ◽  
Xiangyi Chen ◽  
Mingyi Hong ◽  
Chao Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Deep Convolutional Neural Networks ◽  
Battery Capacity ◽  
Li Ion

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

Online estimation of lithium-ion battery capacity using sparse Bayesian learning

2015 ◽  
Vol 289 ◽  
pp. 105-113 ◽  
Author(s):  
Chao Hu ◽  
Gaurav Jain ◽  
Craig Schmidt ◽  
Carrie Strief ◽  
Melani Sullivan
Keyword(s):  
Lithium Ion Battery ◽  
Bayesian Learning ◽  
Lithium Ion ◽  
Sparse Bayesian Learning ◽  
Online Estimation ◽  
Battery Capacity

Heat Transfer and Thermal Stress in a Lithium-Ion Battery

2010 ◽  
Author(s):  
Wei Wu ◽  
Xinran Xiao ◽  
Danghe Shi
Keyword(s):  
Heat Transfer ◽  
Thermal Stress ◽  
Lithium Ion ◽  
Scale Model ◽  
Heat Sources ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Multi Scale ◽  
Induced Stress ◽  
Li Ion

This paper presents a finite element based multi-scale model for a lithium-ion (Li-ion) battery cell. The model considers multi-physics including battery kinetics, diffusion, thermal and stress analysis. In battery thermal analysis, the heat source is critical. In this model, both resistive and entropic heating were considered. Simulations were carried out for a LiC6/LiPF6/LiyMn2O4 cell under a discharge-charge cycle. The heat generations due to these two heat sources were compared. The thermal stress was computed and compared with the intercalation stress for individual battery components. Within the electrode particles, the thermal induced stress was negligible. In the separator, however, the thermal induced stress was comparable or even higher than the stress caused by intercalation deformation of the electrode particles.

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...

Combined Ag and Cu-doping of MnO improves Li-ion battery capacity retention on cycling

2021 ◽  
pp. 130659
Author(s):  
Liang He ◽  
Jimmy Wu ◽  
Derwin Lau ◽  
Charles Hall ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Li Ion Battery ◽  
Capacity Retention ◽  
Cu Doping ◽  
Battery Capacity ◽  
Li Ion

Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Real Time ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Time Estimation ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Estimation Scheme ◽  
Real Time Estimation ◽  
Li Ion

This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.

Effects of SiO2 particles in copper current collector on diffusion induced stresses in layered Li-ion battery electrodes

2021 ◽  
pp. 095440622110036
Author(s):  
Roozbeh Pouyanmehr ◽  
Morteza Pakseresht ◽  
Reza Ansari ◽  
Mohammad Kazem Hassanzadeh-Aghdam
Keyword(s):  
Volume Fraction ◽  
Lithium Ion ◽  
Current Collector ◽  
Limiting Factors ◽  
Li Ion Battery ◽  
Current Collectors ◽  
Effect Of Particle Size ◽  
Li Ion ◽  
Induced Stresses ◽  
Copper Composite

One of the limiting factors in the life of lithium-ion batteries is the diffusion-induced stresses on their electrodes that cause cracking and consequently, failure. Therefore, improving the structure of these electrodes to be able to withstand these stresses is one of the ways that can extend the life of the batteries as well as improve their safety. In this study, the effects of adding graphene nanoplatelets and microparticles into the active plate and current collectors, respectively, on the diffusion induced stresses in both layered and bilayered electrodes are numerically investigated. The micromechanical models are employed to predict the mechanical properties of both graphene nanoplatelet-reinforced Sn-based nanocomposite active plate and silica microparticle-reinforced copper composite current collector. The effect of particle size and volume fraction in the current collector on diffusion induced stresses has been studied. The results show that in electrodes with a higher volume fraction of particles and smaller particle radii, decreased diffusion induced stresses in both the active plate and the current collector are observed. These additions will also result in a significant decrease in the bending of the electrode.

scholarly journals Reliability Analysis of Different Cell Configurations of Lithium ion battery Pack

2019 ◽  
Vol 18 (2) ◽  
pp. 49-56
Author(s):  
Md. Nahian Al Subri Ivan ◽  
Sujit Devnath ◽  
Rethwan Faiz ◽  
Kazi Firoz Ahmed
Keyword(s):  
Mathematical Model ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Series Configuration ◽  
Li Ion ◽  
Parallel Module ◽  
The Mathematical Model ◽  
Parallel Series

To infer and predict the reliability of the remaining useful life of a lithium-ion (Li-ion) battery is very significant in the sectors associated with power source proficiency. As an energy source of electric vehicles (EV), Li-ion battery is getting attention due to its lighter weight and capability of storing higher energy. Problems with the reliability arises while li-ion batteries of higher voltages are required. As in this case several li-ion cells areconnected in series and failure of one cell may cause the failure of the whole battery pack. In this paper, Firstly, the capacity degradation of li-ion cells after each cycle is observed and secondly with the help of MATLAB 2016 a mathematical model is developed using Weibull Probability Distribution and Exponential Distribution to find the reliability of different types of cell configurations of a non-redundant li-ion battery pack. The mathematical model shows that the parallel-series configuration of cells is more reliable than the series configuration of cells. The mathematical model also shows that if the discharge rate (C-rate) remains constant; there could be an optimum number for increasing the cells in the parallel module of a parallel-series onfiguration of cells of a non-redundant li-ion battery pack; after which only increasing the number of cells in parallel module doesn’t increase the reliability of the whole battery pack significantly. 

scholarly journals Charge and Discharge Behaviour of Li-Ion Batteries at Various Temperatures Containing LiCoO2 Nanostructured Cathode Produced by CCSO

2015 ◽  
Vol 15 (4) ◽  
pp. 301 ◽  
Author(s):  
Y.Y. Mamyrbayeva ◽  
R.E. Beissenov ◽  
M.A. Hobosyan ◽  
S.E. Kumekov ◽  
K.S. Martirosyan
Keyword(s):  
Lithium Ion Battery ◽  
Active Material ◽  
Lithium Ion ◽  
Synthetic Approach ◽  
Capacity Fade ◽  
Li Ion Batteries ◽  
Material Loss ◽  
Method Performance ◽  
Battery Capacity ◽  
Li Ion

<p>There are technical barriers for penetration market requesting rechargeable lithium-ion battery packs for portable devices that operate in extreme hot and cold environments. Many portable electronics are used in very cold (-40 °C) environments, and many medical devices need batteries that operate at high temperatures. Conventional Li-ion batteries start to suffer as the temperature drops below 0 °C and the internal impedance of the battery  increases. Battery capacity also reduced during the higher/lower temperatures. The present work describes the laboratory made lithium ion battery behaviour features at different operation temperatures. The pouch-type battery was prepared by exploiting LiCoO<sub>2</sub> cathode material synthesized by novel synthetic approach referred as Carbon Combustion Synthesis of Oxides (CCSO). The main goal of this paper focuses on evaluation of the efficiency of positive electrode produced by CCSO method. Performance studies of battery showed that the capacity fade of pouch type battery increases with increase in temperature. The experimental results demonstrate the dramatic effects on cell self-heating upon electrochemical performance. The study involves an extensive analysis of discharge and charge characteristics of battery at each temperature following 30 cycles. After 10 cycles, the battery cycled at RT and 45 °C showed, the capacity fade of 20% and 25% respectively. The discharge capacity for the battery cycled at 25 °C was found to be higher when compared with the battery cycled at 0 °C and 45 °C. The capacity of the battery also decreases when cycling at low temperatures. It was important time to charge the battery was only 2.5 hours to obtain identical nominal capacity under the charging protocol. The decrease capability of battery cycled at high temperature can be explained with secondary active material loss dominating the other losses.</p>

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