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

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.

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Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles

Journal of Power Sources ◽

10.1016/j.jpowsour.2011.01.005 ◽

2011 ◽

Vol 196 (8) ◽

pp. 3921-3932 ◽

Cited By ~ 225

Author(s):

Yi-Hsien Chiang ◽

Wu-Yang Sean ◽

Jia-Cheng Ke

Keyword(s):

Lithium Ion Batteries ◽

Electric Vehicles ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Internal Resistance ◽

Open Circuit ◽

Online Estimation

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

Computational Intelligence and Neuroscience ◽

10.1155/2015/918305 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 13

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.

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

Sustainability ◽

10.3390/su132313333 ◽

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.

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A comprehensive study on the degradation of lithium-ion batteries during calendar ageing: The internal resistance increase

2016 IEEE Energy Conversion Congress and Exposition (ECCE) ◽

10.1109/ecce.2016.7854664 ◽

2016 ◽

Cited By ~ 4

Author(s):

Daniel-Ioan Stroe ◽

Maciej Swierczynski ◽

Soren Knudsen Kar ◽

Remus Teodorescu

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion ◽

Internal Resistance ◽

Resistance Increase ◽

Comprehensive Study

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

ECMS 2021 Proceedings edited by Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela ◽

10.7148/2021-0235 ◽

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.

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

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211058563 ◽

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.

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