Obtaining a Model for the Voltage and Temperature of the US18650VTC6 Series Lithium-ion Battery in Constant Current Discharge Mode from the Analysis of Physical and Chemical Processes in the Accumulator

2021 ◽  
Author(s):  
Igor E. Starostin ◽  
Sergey P. Khalyutin ◽  
Albert O. Davidov ◽  
Elena A. Punt ◽  
Viktoriya I. Pavlova
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Chemical Processes ◽  
Constant Current ◽  
Discharge Mode ◽  
Current Discharge ◽  
Physical And Chemical

Effect of electrode physical and chemical properties on lithium-ion battery performance

2013 ◽  
Vol 37 (14) ◽  
pp. 1723-1736 ◽  
Author(s):  
Victor Chabot ◽  
Siamak Farhad ◽  
Zhongwei Chen ◽  
Alan S. Fung ◽  
Aiping Yu ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Chemical Properties ◽  
Lithium Ion ◽  
Physical And Chemical Properties ◽  
Physical And Chemical ◽  
And Chemical Properties

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

scholarly journals Add-On Type Pulse Charger for Quick Charging Li-Ion Batteries

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 227 ◽  
Author(s):  
Bongwoo Kwak ◽  
Myungbok Kim ◽  
Jonghoon Kim
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Current Method ◽  
Constant Current ◽  
Experimental Conditions ◽  
Battery Charging ◽  
Long Run ◽  
Pulse Charging ◽  
Pulse Charge ◽  
Battery Packs

In this paper, an add-on type pulse charger is proposed to shorten the charging time of a lithium ion battery. To evaluate the performance of the proposed pulse charge method, an add-on type pulse charger prototype is designed and implemented. Pulse charging is applied to 18650 cylindrical lithium ion battery packs with 10 series and 2 parallel structures. The proposed pulse charger is controlled by pulse duty, frequency and magnitude. Various experimental conditions are applied to optimize the charging parameters of the pulse charging technique. Battery charging data are analyzed according to the current magnitude and duty at 500 Hz and 1000 Hz and 2000 Hz frequency conditions. The proposed system is similar to the charging speed of the constant current method under new battery conditions. However, it was confirmed that as the battery performance is degraded, the charging speed due to pulse charging increases. Thus, in applications where battery charging/discharging occurs frequently, the proposed pulse charger has the advantage of fast charging in the long run over conventional constant current (CC) chargers.

scholarly journals Quantum chemical calculations of lithium-ion battery electrolyte and interphase species

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Evan Walter Clark Spotte-Smith ◽  
Samuel M. Blau ◽  
Xiaowei Xie ◽  
Hetal D. Patel ◽  
Mingjian Wen ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
First Principles ◽  
Quantum Chemical ◽  
State Of The Art ◽  
Solid Electrolyte Interphase ◽  
Lithium Ion ◽  
Chemical Processes ◽  
Fundamental Understanding ◽  
Battery Electrolyte ◽  
Unique Species

AbstractLithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte (LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural, thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.

scholarly journals Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species

2021 ◽  
Author(s):  
Evan Walter Clark Spotte-Smith ◽  
Samuel Blau ◽  
Xiaowei Xie ◽  
Hetal Patel ◽  
Mingjian Wen ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
First Principles ◽  
Quantum Chemical ◽  
State Of The Art ◽  
Solid Electrolyte Interphase ◽  
Lithium Ion ◽  
Chemical Processes ◽  
Fundamental Understanding ◽  
Battery Electrolyte ◽  
Unique Species

Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte(LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural,thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.

Sign in / Sign up

Export Citation Format

Share Document