scholarly journals Analysis of Peukert and Liebenow Equations Use for Evaluation of Capacity Released by Lithium-Ion Batteries

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1753
Author(s):  
Nataliya N. Yazvinskaya ◽  
Nikolay E. Galushkin ◽  
Dmitriy V. Ruslyakov ◽  
Dmitriy N. Galushkin
Keyword(s):  
Lithium Ion Batteries ◽  
Discharge Current ◽  
Inflection Point ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Electrochemical Systems ◽  
Lead Acid Batteries ◽  
Battery Capacity ◽  
Experimental Function ◽  
Small Discharge

The Peukert and Liebenow equations were obtained from experimental studies of lead–acid batteries. Currently, they are used to evaluate capacity released by batteries of other electrochemical systems (alkaline, lithium-ion, etc.), as well. In this paper, it is experimentally proved that for lithium-ion batteries, the Peukert equation can be used in two intervals of the discharge currents. The first interval includes currents from 0.2Cn up to the first inflection point of the experimental function of the battery capacity dependence on the discharge currents C(i). The second interval covers currents from the second inflection point of the experimental function C(i) up to the maximum currents that were used in the experiments. For some lithium-ion batteries, the range of low discharge currents, where the Peukert equation is applicable, is quite large and often completely covers the range of the discharge currents used in practice for these batteries. Therefore, many authors, when estimating the capacity of lithium-ion batteries, use the Peukert equation. However, the research in this paper shows that for lithium-ion batteries, the use of the Peukert equation is limited to the two discharge current ranges indicated above. Unlike the Peukert equation, the Liebenow equation can be used only in the range of small discharge currents from zero to the first inflection point of the experimental function C(i).

Analysis of the Possibility of Use Lithium - Ion as a Starting Battery on the Ship Engine Room

2015 ◽  
Vol 236 ◽  
pp. 106-112
Author(s):  
Grzegorz Grzeczka ◽  
Paweł Swoboda
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
The Other ◽  
Electrochemical Systems ◽  
Lead Acid Batteries ◽  
Engine Room ◽  
Other Hand ◽  
Start Up ◽  
Electrochemical Parameters ◽  
Lead Acid

The most commonly used starter batteries for ship engine rooms are lead acid systems. Lead acid batters have the lowest electrochemical parameters from all other modern electrochemical systems. On the other hand their biggest advantage is the price of the cell which is much lower comparing to other electrochemical systems. Due to fact that the lithium – ion batteries are very widely used and constantly developed this technology is starting to be promising as an alternative for lead acid batteries for starter applications. Because of this there is a need to verify if the lithium - ion technology can be used for start-up and power backup systems and how will it affect the construction of the engine room and those systems. In order to determine the potential energetic requirements during the design of starter systems in an backup engine room with the use of lithium – ion batteries, in the article the analytic of their performance was conducted with comparison of other electrochemical systems.

scholarly journals Effect of dynamic loads and vibrations on lithium-ion batteries

2021 ◽  
pp. 146134842110081
Author(s):  
Xia Hua ◽  
Alan Thomas
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Pack ◽  
Dynamic Loads ◽  
Electrical Performance ◽  
Mechanical Degradation ◽  
Energy Storage Devices ◽  
Battery Cell

Lithium-ion batteries are being increasingly used as the main energy storage devices in modern mobile applications, including modern spacecrafts, satellites, and electric vehicles, in which consistent and severe vibrations exist. As the lithium-ion battery market share grows, so must our understanding of the effect of mechanical vibrations and shocks on the electrical performance and mechanical properties of such batteries. Only a few recent studies investigated the effect of vibrations on the degradation and fatigue of battery cell materials as well as the effect of vibrations on the battery pack structure. This review focused on the recent progress in determining the effect of dynamic loads and vibrations on lithium-ion batteries to advance the understanding of lithium-ion battery systems. Theoretical, computational, and experimental studies conducted in both academia and industry in the past few years are reviewed herein. Although the effect of dynamic loads and random vibrations on the mechanical behavior of battery pack structures has been investigated and the correlation between vibration and the battery cell electrical performance has been determined to support the development of more robust electrical systems, it is still necessary to clarify the mechanical degradation mechanisms that affect the electrical performance and safety of battery cells.

scholarly journals Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chaolong Zhang ◽  
Yigang He ◽  
Lifeng Yuan ◽  
Sheng Xiang ◽  
Jinping Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Noise Pollution ◽  
Relevance Vector Machine ◽  
Wavelet Denoising ◽  
Remaining Useful Life ◽  
Electronic Systems ◽  
De Algorithm ◽  
Battery Capacity ◽  
Capacity Data

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery’s remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.

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.

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 Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2856 ◽  
Author(s):  
Lijun Zhang ◽  
Zhongqiang Mu ◽  
Xiangyu Gao
Keyword(s):  
Lithium Ion Batteries ◽  
Influencing Factor ◽  
Lithium Ion ◽  
Internal Resistance ◽  
Performance Study ◽  
Vibration Direction ◽  
Vibration Condition ◽  
Coupling Conditions ◽  
Battery Capacity ◽  
Vibration Coupling

At present, a variety of standardized 18650 commercial cylindrical lithium-ion batteries are widely used in new energy automotive industries. In this paper, the Panasonic NCR18650PF cylindrical lithium-ion batteries were studied. The NEWWARE BTS4000 battery test platform is used to test the electrical performances under temperature, vibration and temperature-vibration coupling conditions. Under the temperature conditions, the discharge capacity of the same battery at the low temperature was only 85.9% of that at the high temperature. Under the vibration condition, mathematical statistics methods (the Wilcoxon Rank-Sum test and the Kruskal-Wallis test) were used to analyze changes of the battery capacity and the internal resistance. Changes at a confidence level of 95% in the capacity and the internal resistance were considered to be significantly different between the vibration conditions at 5 Hz, 10 Hz, 20 Hz and 30 Hz versus the non-vibration condition. The internal resistance of the battery under the Y-direction vibration was the largest, and the difference was significant. Under the temperature-vibration coupling conditions, the orthogonal table L9 (34) was designed. It was found out that three factors were arranged in order of temperature, vibration frequency and vibration direction. Among them, the temperature factor is the main influencing factor affecting the performance of lithium-ion batteries.

scholarly journals Electrochemical Performance of a Lithium Ion Battery with Different Nanoporous Current Collectors

Batteries ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 21 ◽  
Author(s):  
Huajun Feng ◽  
Yuan Chen ◽  
Yihua Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Positive Impact ◽  
Lithium Ion ◽  
Current Collector ◽  
Aluminum Foil ◽  
Nanoporous Structure ◽  
Current Collectors ◽  
Copper Foam ◽  
Ultrasonic Time ◽  
Battery Capacity

In this work, we use ultrasonication and chemical etching agents to assist preparation of metal current collectors with nano-scale pores on the surface. Four different current collectors (copper foil, copper foam, aluminum foil, and aluminum foam) are prepared. The preparation parameters, ultrasonic time and etching agent concentration, are investigated and optimized accordingly. The morphologies of the as-prepared current collectors are observed under a scanning electronic microscope. Soft-packed lithium ion batteries with various current collectors are fabricated and tested. The prepared lithium ion batteries show good long-term cycle stability. The nanoporous structure of the current collector has little impact on the improvement of battery capacity under slow charging/discharging rates but has a positive impact on capacity retention under fast charging/discharging rates.

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