Lithium-ion (Li-ion) battery technology evolves to serve an extended range of telecom applications

2011 ◽  
Author(s):  
Joel Brunarie ◽  
Anne-Marie Billard ◽  
Stuart Lansburg ◽  
Mathieu Belle
Keyword(s):  
Lithium Ion ◽  
Li Ion Battery ◽  
Extended Range ◽  
Li Ion ◽  
Battery Technology

scholarly journals Review: Li-ion Batteries: Basics, Advancement, Challenges & Applications in Military

2021 ◽  
Vol 9 (8) ◽  
pp. 3009-3021
Author(s):  
Lt. Col Pankaj Kushwaha
Keyword(s):  
Lithium Ion ◽  
Armed Forces ◽  
High Energy ◽  
High Energy Density ◽  
Great Promise ◽  
Portable Devices ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Li Ion ◽  
Battery Technology

Abstract: Li-ion battery technology has become very important in recent years as these batteries show great promise as power source. They power most of today’s portable devices and seem to overcome the psychological barriers against the use of such high energy density devices on a larger scale. Lithium-ion batteries are being widely used in military applications for over a decade. These man portable applications include tactical radios, thermal imagers, ECM, ESM, and portable computing. In the next five years, due to the rapid inventions going on in li-ion batteries, the usage of lithium batteries will further expand to heavy-duty platforms, such as military vehicles, boats, shelter applications, aircraft and missiles. The aim of this paper is to review key aspects of Li-ion batteries, the basic science behind their operation, the most relevant components, anodes, cathodes, electrolyte solution as well as important future directions for R&D of advanced Li-ion batteries for demanding use in Indian Armed Forces which are deployed in very harsh conditions across the country. Keywords: Li-ion Battery, NiCd battery

Modelling and Performance Analysis of Different Types of Li-Ion Battery

2020 ◽  
Author(s):  
C. Santhi Durganjali ◽  
Harini Raghavan ◽  
Sudha Radhika
Keyword(s):  
Electrode Materials ◽  
Lithium Ion ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Dimensional Model ◽  
Depth Of Discharge ◽  
Different Types ◽  
Li Ion ◽  
And Performance ◽  
Battery Technology

Abstract Lithium ion batteries are at present, the most widely used battery technology in the world. Every battery’s performance is characterized by certain parameters like the State of Charge, and Depth of Discharge, C-rate etc. To explore the possibility of more efficient types of Li-ion batteries for more applications a wide demand in identifying, modeling and testing of different possible combinations of electrode materials and electrolytes of Li-ion batteries arose. Taking this demand into consideration authors of this paper focus on the modeling and simulation of a wide variety of possible combinations of Li-ion battery in a 2-dimensional model. In addition to that, a thermal model of a cylindrical lithium ion battery was built in 3-dimensions and was validated with experimental data. The simulations were carried out on COMSOL:Multiphysics.

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...

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.

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.

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. 

Cationic covalent organic framework based all-solid-state electrolytes

2020 ◽  
Vol 4 (4) ◽  
pp. 1164-1173 ◽  
Author(s):  
Zhen Li ◽  
Zhi-Wei Liu ◽  
Zhen-Jie Mu ◽  
Chen Cao ◽  
Zeyu Li ◽  
...  
Keyword(s):  
High Temperature ◽  
Solid State ◽  
Solid Electrolytes ◽  
Lithium Ion ◽  
Ion Conductivity ◽  
Li Ion Battery ◽  
Covalent Organic Framework ◽  
Li Ion ◽  
Lithium Ion Conductivity ◽  
Organic Framework

Two new imidazolium-based cationic COFs were synthesized and employed as all-solid electrolytes, and exhibited high lithium ion conductivity at high temperature. The assembled Li-ion battery displays preferable battery performance at 353 K.

scholarly journals Scheduled Pre-Heating of Li-Ion Battery Packs for Balanced Temperature and State-of-Charge Distribution

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2212
Author(s):  
Hien Vu ◽  
Donghwa Shin
Keyword(s):  
Thermal Characteristics ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Pack ◽  
Li Ion Battery ◽  
Internal Heating ◽  
Heating Efficiency ◽  
Battery Packs ◽  
Unit Cells ◽  
Li Ion

Lithium-ion batteries exhibit significant performance degradation such as power/energy capacity loss and life cycle reduction in low-temperature conditions. Hence, the Li-ion battery pack is heated before usage to enhance its performance and lifetime. Recently, many internal heating methods have been proposed to provide fast and efficient pre-heating. However, the proposed methods only consider a combination of unit cells while the internal heating should be implemented for multiple groups within a battery pack. In this study, we investigated the possibility of timing control to simultaneously obtain balanced temperature and state of charge (SOC) between each cell by considering geometrical and thermal characteristics of the battery pack. The proposed method schedules the order and timing of the charge/discharge period for geometrical groups in a battery pack during internal pre-heating. We performed a pack-level simulation with realistic electro-thermal parameters of the unit battery cells by using the mutual pulse heating strategy for multi-layer geometry to acquire the highest heating efficiency. The simulation results for heating from −30 ∘ C to 10 ∘ C indicated that a balanced temperature-SOC status can be achieved via the proposed method. The temperature difference can be decreased to 0.38 ∘ C and 0.19% of the SOC difference in a heating range of 40 ∘ C with only a maximum SOC loss of 2.71% at the end of pre-heating.

scholarly journals Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
An Wen ◽  
Jinhao Meng ◽  
Jichang Peng ◽  
Lei Cai ◽  
Qian Xiao
Keyword(s):  
Instrumental Variable ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Parameters Estimation ◽  
Measurement Noise ◽  
Recursive Least Squares ◽  
Ar Model ◽  
Noise Model ◽  
Li Ion Battery ◽  
Li Ion

Refined Instrumental Variable (RIV) estimation is applied to online identify the parameters of the Equivalent Circuit Model (ECM) for Lithium-ion (Li-ion) battery in this paper, which enables accurate parameters estimation with the measurement noise. Since the traditional Recursive Least Squares (RLS) estimation is extremely sensitive to the noise, the parameters in the ECM may fail to converge to their true values under the measurement noise. The RIV estimation is implemented in a bootstrap form, which alternates between the estimation in the system model and the noise model. The Box-Jenkins model of the Li-ion battery transformed from the two RC ECM is selected as the transfer function model for the RIV estimation in this paper. The errors of the two RC ECM are independently generated by the residual of high-order Auto Regressive (AR) model estimation. With the benefit of a series of auxiliary models, the data filtering technology can prefilter the measurement and increase the robustness of the parameters against the noise. Reasonable parameters are possible to be obtained regardless of the noise in the measurement by RIV. Simulation and experimental tests on a LiFePO4 battery validate the efficiency of RIV for parameter online identification compared with traditional RLS.

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