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

Layered LiNi1-xCoxO2 is one of the promising cathode materials for Li-ion battery application. However, the Ni rich cathode materials exhibit low capacity and bad capacity retention. This is due to factors such as disorder and structural instability when Li is removed during charge-discharge. Overlithiation of cathode materials is expected to improve the cation ordering and structural stability. Good cation ordering will increase the battery capacity. During charge-discharge, the irreversible Li+ loss can be replaced to a certain extent by the interstitial Li+ ions in the lattice of the LixNi0.8Co0.2O2 material. This helps reduce capacity fading of the cathode materials. In this work the overlithiation of LiNi0.8Co0.2O2 is done by interstitially doping Li+ in the LiNi0.8Co0.2O2 materials producing Li1.05Ni0.8Co0.2O2 and Li1.1Ni0.8Co0.2O2. Results showthat the performance of the overlithiated LiNi0.8Co0.2O2 materials is better than pure LiNi0.8Co0.2O2.

Download Full-text

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

Volume 2A: 44th Design Automation Conference ◽

10.1115/detc2018-86347 ◽

2018 ◽

Cited By ~ 1

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.

Download Full-text

Li-ion battery capacity cycling fading dynamics cognition: A stochastic approach

Energy ◽

10.1016/j.energy.2017.06.167 ◽

2017 ◽

Vol 137 ◽

pp. 251-259 ◽

Cited By ~ 2

Author(s):

Chen Lu ◽

Lipin Zhang ◽

Jian Ma ◽

Zihan Chen ◽

Laifa Tao ◽

...

Keyword(s):

Stochastic Approach ◽

Li Ion Battery ◽

Battery Capacity ◽

Li Ion

Download Full-text

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

Volume 2A: 41st Design Automation Conference ◽

10.1115/detc2015-46964 ◽

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.

Download Full-text

Underpotential lithium plating on graphite anodes caused by temperature heterogeneity

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2009221117 ◽

2020 ◽

Vol 117 (47) ◽

pp. 29453-29461

Author(s):

Hansen Wang ◽

Yangying Zhu ◽

Sang Cheol Kim ◽

Allen Pei ◽

Yanbin Li ◽

...

Keyword(s):

Short Circuit ◽

Equilibrium Potential ◽

Li Ion Batteries ◽

Li Ion Battery ◽

Slow Kinetics ◽

Battery Capacity ◽

Fast Charging ◽

Graphite Intercalation ◽

Graphite Anodes ◽

Li Ion

Rechargeability and operational safety of commercial lithium (Li)-ion batteries demand further improvement. Plating of metallic Li on graphite anodes is a critical reason for Li-ion battery capacity decay and short circuit. It is generally believed that Li plating is caused by the slow kinetics of graphite intercalation, but in this paper, we demonstrate that thermodynamics also serves a crucial role. We show that a nonuniform temperature distribution within the battery can make local plating of Li above 0 V vs. Li0/Li+(room temperature) thermodynamically favorable. This phenomenon is caused by temperature-dependent shifts of the equilibrium potential of Li0/Li+. Supported by simulation results, we confirm the likelihood of this failure mechanism during commercial Li-ion battery operation, including both slow and fast charging conditions. This work furthers the understanding of nonuniform Li plating and will inspire future studies to prolong the cycling lifetime of Li-ion batteries.

Download Full-text

Preparing LiFePO4 Using Recovered Materials from Waste Li-Ion Battery

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.2940 ◽

2013 ◽

Vol 726-731 ◽

pp. 2940-2944 ◽

Cited By ~ 1

Author(s):

Feng Pei ◽

Yue Wu ◽

Wen Hua Zhang ◽

Xu Tian ◽

Ji Yu

Keyword(s):

Solid State ◽

Solid State Reaction ◽

Raw Material ◽

Molar Ratio ◽

Li Ion Battery ◽

Optimal Conditions ◽

Initial Discharge Capacity ◽

Capacity Retention ◽

Initial Discharge ◽

Li Ion

LiFePO4 was prepared using recovered materials from waste Li-ion battery. The recovered materials after treatment was mixed with Li2CO3, Fe (NO3) 3·9H2O and NH4H2PO4 to adjust the Li/Fe/P molar ratio equal to 1.05/1/1. The raw material was mixed with super-p and calcined in muffle to get LiFePO4 by a solid-state reaction. Optimal conditions were: 700°C, N2 ambience, 10h, and Fe/C=1/1.5 (mol). The characterization results showed that the product was irregular particles with size 5-10μm and good dispersion. When discharged in the range of 2.2~4.2V, the initial discharge capacity was 141.4mAh/g at 0.1C, 103.1mAh/g at 1C. The capacity retention was 97.2% after 300 cycles at 1C showing satisfactory stability.

Download Full-text