Evaluation of the influence of high-power charging cycles on the capacity degradation of lithium-ion batteries under various temperatures

2021 ◽  
pp. 014233122110585
Author(s):  
Xiaogang Wu ◽  
Yinlong Xia ◽  
Jiuyu Du ◽  
Kun Zhang ◽  
Jinlei Sun
Keyword(s):  
Lithium Ion Batteries ◽  
High Power ◽  
Electrode Materials ◽  
Electrochemical Impedance ◽  
Degradation Mechanism ◽  
Solid Electrolyte Interphase ◽  
Lithium Ion ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Charging Current

High-power-charging (HPC) behavior and extreme ambient temperature not only pose security risks on the operation of lithium-ion batteries but also lead to capacity degradation. Exploring the degradation mechanism under these two conditions is very important for safe and rational use of lithium-ion batteries. To investigate the influence of various charging-current rates on the battery-capacity degradation in a wide temperature range, a cycle-aging test is carried out. Then, the effects of HPC on the capacity degradation at various temperatures are analyzed and discussed using incremental capacity analysis and electrochemical impedance spectroscopy. The analysis results show that a large number of lithium ions accelerate the deintercalation when the HPC cycle rate exceeds 3 C, making the solid electrolyte interphase at the negative surface unstable and vulnerable to destruction, which results in irreversible consumption of active lithium. In addition, the decomposition of electrolyte is significantly promoted when the HPC temperature is more than 30°C, resulting in accelerated consumption of electrode materials and active lithium, which are the main reasons for the capacity degradation of lithium-ion batteries during HPC under various temperatures.

scholarly journals Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

2021 ◽  
Vol 13 (23) ◽  
pp. 13333
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Recurrent Neural Network ◽  
Single Channel ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Channel Input ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.

Capacity Loss Estimation For Li-Ion Batteries Based On A Semi-Empirical Model

2021 ◽  
Author(s):  
Mohammed Rabah ◽  
Eero Immonen ◽  
Sajad Shahsavari ◽  
Mohammad-Hashem Haghbayan ◽  
Kirill Murashko ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Empirical Model ◽  
Lithium Ion ◽  
Average Error ◽  
Li Ion Batteries ◽  
Capacity Loss ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Li Ion ◽  
Semi Empirical

Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the Capacity Loss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to redict the Capacity Loss with a high accuracy, battery operation data from different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained models were then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss is less than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.

Sustainable and Low-Cost Lithium-Ion Batteries: Nonconventional Electrode Chemistries and State of Health Characterization

2021 ◽  
Author(s):  
Ruoxu Shang ◽  
Taner Zerrin ◽  
Bo Dong ◽  
Cengiz S. Ozkan ◽  
Mihrimah Ozkan
Keyword(s):  
Lithium Ion Batteries ◽  
Electrode Materials ◽  
Electrochemical Impedance ◽  
Accurate Determination ◽  
Lithium Ion ◽  
High Energy ◽  
Beach Sand ◽  
State Of Health ◽  
Sulfur Silicon ◽  
Material Sources

With the advancements in portable electronics and electric vehicle (EV) applications, the demand for lithium-ion batteries (LIBs) with high energy densities is ever increasing. Battery-powered transportation is being adopted more frequently due to its potential to enable a more sustainable society by reducing vehicle emissions from fossil fuels. There has been exponential growth in the need for high-capacity LIBs in all types of EVs, including hybrid and full electric automobiles, e-bikes, and drones, as well as electric tools, cell phones, tablets, and, more recently, house storage; this growth significantly increases the consumption of source material commodities,especially cobalt. Despite its drop in price in the last couple of years due to increased mining, cobalt remains expensive, and its price increase has gained momentum again compared toother electrode materials due to higher demand. Moreover, its toxicity and difficult mining practices could result in many problems, including excessive carbon dioxide and nitrogendioxide emission along with a possible much higher demand in the long term. This provides a strong motivation to explore alternatives to battery source materials. In this article, we present a selection of our important works on LIBs, with a focus on alternative electrode chemistries by using abundant and sustainable material sources. As alternatives to traditional graphite-based anodes, we demonstrate the successful use of both silicon electrodes derived from beach sand and waste glass and carbon electrodes derived from portobello mushroom and waste plastic precursors. In addition, we demonstrate stable cycling of batteries with nonconventional electrode chemistries, such as lithium-sulfur with TiO2-coated sulfur electrodes and sulfur-silicon full cell batteries with integrated lithium sources. Batteries prepared by sustainable methods not only perform better than conventional ones but also result in reduced costs. Since accurate determination of battery state of health is another important challenge, we further present our electrochemical impedance spectroscopy-based analysis of LIBs, which could potentially be utilized in safety evaluations of current and next-generation LIBs.

scholarly journals Ceramic-Coated Separator to Enhance Cycling Performance of Lithium-ion Batteries at High Current Density

2021 ◽  
Vol 59 (11) ◽  
pp. 813-820
Author(s):  
Kyusang Cho ◽  
Chandran Balamurugan ◽  
Hana Im ◽  
Hyeong-Jin Kim
Keyword(s):  
Solid Electrolyte ◽  
Lithium Ion Batteries ◽  
High Performance ◽  
Electrochemical Impedance ◽  
High Current Density ◽  
Solid Electrolyte Interphase ◽  
Lithium Ion ◽  
Green Energy ◽  
Cycle Performance ◽  
Polypropylene Separator

Given the global demand for green energy, the battery industry is positioned to be an important future technology. Lithium-ion batteries (LIBs), which are the most widely used battery in the market, are the focus of various research and development efforts, from materials to systems, that seek to improve their performance. The separator is one of the core materials in LIBs and is a significant factor in the lifespan of high-performance batteries. To improve the performance of present LIBs, electrochemical testing and related surface analyses of the separator is essential. In this paper, we prepared a ceramic (Boehmite, γ-AlOOH) coated polypropylene separator and a porous polyimide separator to compare their electrochemical properties with a commercialized polypropylene (PP) separator. The prepared separators were assembled into nickelmanganese-cobalt (NMC) cathode half-cell and full-cell lithium-ion batteries. Their cycling performances were evaluated using differential capacity and electrochemical impedance spectroscopy with ethylene carbonate:dimethylcarbonate (EC:DMC) electrolyte. The ceramic coated polypropylene separator exhibited the best cycle performance at a high 5 C rate, with high ionic conductivity and less resistive solid electrolyte interphase. Also, it was confirmed that a separator solid electrolyte interface (SSEI) layer formed on the separator with cycle repetition, and it was also confirmed that this phenomenon determined the cycle life of the battery depending on the electrolyte.

scholarly journals Enhanced electrochemical performance of RuO2 doped LiNi1/3Mn1/3Co1/3O2 cathode material for Lithium-ion battery

2021 ◽  
Author(s):  
K. Kalaiselvi ◽  
S. Premlatha ◽  
M. Raju ◽  
Paruthimal Kalaignan Guruvaiah
Keyword(s):  
Electrochemical Performance ◽  
Cathode Material ◽  
Lithium Ion Batteries ◽  
Electrode Materials ◽  
Electrochemical Impedance ◽  
Sol Gel ◽  
Lithium Ion ◽  
X Ray Diffraction ◽  
High Discharge Capacity ◽  
Nitrate Precursor

Abstract LiNi1/3Mn1/3Co1/3O2 as a promising cathode material for lithium-ion batteries was synthesized by a sol-gel method using nitrate precursor calcined at 800°C for 10 hours. The crystallite nature of samples is confirmed from X-ray diffraction analysis. SEM and TEM analyses were used to investigate the surface morphology of the prepared samples. It was found that, highly crystalline polyhedral RuO2 nanoparticles are well doped on the surface of pristine LiNi1/3Mn1/3Co1/3O2 with a size of about approximately 200 nm. The chemical composition of the prepared samples was characterized by EDX and XPS analyses. The electrochemical performance of the proposed material was studied by cyclic voltammetry and charge/discharge analyses. The electrode kinetics of the samples was studied by electrochemical impedance spectroscopy. The developed RuO2 doping may provide an effective strategy to design and synthesize the advanced electrode materials for lithium ion batteries. The doping strategy has dramatically increased the capacity retention from 74 % to 90% with a high discharge capacity of 251.2 mAhg− 1. 3 % RuO2-doped LiNi1/3Mn1/3Co1/3O2 cathode materials have showed the similar characteristics of two potential plateaus obtained at 2.8 and 4.2 V compared with un doped electrode cathode material. These results revealed the enhanced performance of RuO2- doped LiNi1/3Mn1/3Co1/3O2 during insertion and extraction of lithium ions compared to pristine material.

Hierarchically Porous Monolithic LiFePO4/Carbon Composite Electrode Materials for High Power Lithium Ion Batteries

2009 ◽  
Vol 21 (21) ◽  
pp. 5300-5306 ◽  
Author(s):  
Cara M. Doherty ◽  
Rachel A. Caruso ◽  
Bernd M. Smarsly ◽  
Philipp Adelhelm ◽  
Calum J. Drummond
Keyword(s):  
Lithium Ion Batteries ◽  
High Power ◽  
Composite Electrode ◽  
Electrode Materials ◽  
Lithium Ion ◽  
Carbon Composite ◽  
Hierarchically Porous ◽  
Carbon Composite Electrode

State of Health Estimation of Lithium-Ion Batteries Based on Combination of Gaussian Distribution Data and Least Squares Support Vector Machines Regression

2018 ◽  
Vol 929 ◽  
pp. 93-102
Author(s):  
Didik Djoko Susilo ◽  
Achmad Widodo ◽  
Toni Prahasto ◽  
Muhammad Nizam
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Support Vector ◽  
Degradation Data ◽  
Starting Point ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life

Lithium-ion batteries play a critical role in the reliability and safety of a system. Battery health monitoring and remaining useful life (RUL) prediction are needed to prevent catastrophic failure of the battery. The aim of this research is to develop a data-driven method to monitor the batteries state of health and predict their RUL by using the battery capacity degradation data. This paper also investigated the effect of prediction starting point to the RUL prediction error. One of the data-driven method drawbacks is the need of a large amount of data to obtain accurate prediction. This paper proposed a method to generate a series of degradation data that follow the Gaussian distribution based on limited battery capacity degradation data. The prognostic model was constructed from the new data using least square support vector machine (LSSVM) regression. The remaining useful life prediction was carried out by extrapolating the model until reach the end of life threshold. The method was applied to three differences lithium-ion batteries capacity data. The results showed that the proposed method has good performance. The method can predict the lithium-ion batteries RUL with a small error, and the optimal RUL starting point was found at the point where the battery has experienced the highest capacity recovery due to the self-recharge phenomenon.

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