scholarly journals A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

2019 ◽  
Vol 2 (4) ◽  
pp. 263-275 ◽  
Author(s):  
Xuebing Han ◽  
Xuning Feng ◽  
Minggao Ouyang ◽  
Languang Lu ◽  
Jianqiu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Electrode Materials ◽  
Lithium Ion ◽  
Curve Analysis ◽  
Constant Current ◽  
Battery Management ◽  
State Of Health ◽  
Voltage Curve ◽  
Battery Capacity ◽  
Li Ion

AbstractLithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

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.

State-of-Health Online Estimation for Li-Ion Battery

2020 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Liu Fang ◽  
◽  
Liu Xinyi ◽  
Su Weixing ◽  
Chen Hanning ◽  
...  
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Battery Management ◽  
State Of Health ◽  
Li Ion Battery ◽  
Improved Genetic Algorithm ◽  
Battery Management Systems ◽  
Li Ion

To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.

scholarly journals Basic Medium Heterogeneous Solution Synthesis of α-MnO2 Nanoflakes as an Anode or Cathode in Half Cell Configuration (vs. Lithium) of Li-Ion Batteries

Nanomaterials ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 608 ◽  
Author(s):  
Kyungho Kim ◽  
Geoffrey Daniel ◽  
Vadim Kessler ◽  
Gulaim Seisenbaeva ◽  
Vilas Pol
Keyword(s):  
Electrode Materials ◽  
Lithium Ion ◽  
Thermal Reduction ◽  
Constant Current ◽  
Basic Medium ◽  
Solution Synthesis ◽  
Hydrothermal Conditions ◽  
Step Procedure ◽  
Li Ion ◽  
Discharge Capacities

Nano α-MnO2 is usually synthesized under hydrothermal conditions in acidic medium, which results in materials easily undergoing thermal reduction and offers single crystals often over 100 nm in size. In this study, α-MnO2 built up of inter-grown ultra-small nanoflakes with 10 nm thickness was produced in a rapid two-step procedure starting via partial reduction in solution in basic medium subsequently followed by co-proportionation in thermal treatment. This approach offers phase-pure α-MnO2 doped with potassium (cryptomelane type K0.25Mn8O16 structure) demonstrating considerable chemical and thermal stability. The reaction pathways leading to this new morphology and structure have been discussed. The MnO2 electrodes produced from obtained nanostructures were tested as electrodes of lithium ion batteries delivering initial discharge capacities of 968 mAh g−1 for anode (0 to 2.0 V) and 317 mAh g−1 for cathode (1.5 to 3.5 V) at 20 mA g−1 current density. At constant current of 100 mA g−1, stable cycling of anode achieving 660 mAh g−1 and 145 mAh g−1 for cathode after 200 cycles is recorded. Post diagnostic analysis of cycled electrodes confirmed the electrode materials stability and structural properties.

scholarly journals A Segregated Approach for Modeling the Electrochemistry in the 3-D Microstructure of Li-Ion Batteries and Its Acceleration Using Block Preconditioners

2021 ◽  
Vol 86 (3) ◽  
Author(s):  
Jeffery M. Allen ◽  
Justin Chang ◽  
Francois L. E. Usseglio-Viretta ◽  
Peter Graf ◽  
Kandler Smith
Keyword(s):  
Degrees Of Freedom ◽  
Electrode Materials ◽  
Lithium Ion ◽  
Automotive Application ◽  
System Of Equations ◽  
Strongly Correlated ◽  
Electrolyte Depletion ◽  
Li Ion ◽  
Block Preconditioners ◽  
Electrochemical Model

AbstractBattery performance is strongly correlated with electrode microstructure. Electrode materials for lithium-ion batteries have complex microstructure geometries that require millions of degrees of freedom to solve the electrochemical system at the microstructure scale. A fast-iterative solver with an appropriate preconditioner is then required to simulate large representative volume in a reasonable time. In this work, a finite element electrochemical model is developed to resolve the concentration and potential within the electrode active materials and the electrolyte domains at the microstructure scale, with an emphasis on numerical stability and scaling performances. The block Gauss-Seidel (BGS) numerical method is implemented because the system of equations within the electrodes is coupled only through the nonlinear Butler–Volmer equation, which governs the electrochemical reaction at the interface between the domains. The best solution strategy found in this work consists of splitting the system into two blocks—one for the concentration and one for the potential field—and then performing block generalized minimal residual preconditioned with algebraic multigrid, using the FEniCS and the Portable, Extensible Toolkit for Scientific Computation libraries. Significant improvements in terms of time to solution (six times faster) and memory usage (halving) are achieved compared with the MUltifrontal Massively Parallel sparse direct Solver. Additionally, BGS experiences decent strong parallel scaling within the electrode domains. Last, the system of equations is modified to specifically address numerical instability induced by electrolyte depletion, which is particularly valuable for simulating fast-charge scenarios relevant for automotive application.

A novel eutectic solvent precursor for efficiently preparing N-doped hierarchically porous carbon nanosheets with unique surface functional groups and micro-pores towards dual-carbon lithium-ion capacitors

2021 ◽  
Author(s):  
Kaixiang Zou ◽  
Yuanfu Deng ◽  
Weijing Wu ◽  
Shiwei Zhang ◽  
Guohua Chen
Keyword(s):  
Functional Groups ◽  
Porous Carbon ◽  
High Performance ◽  
Electrode Materials ◽  
Lithium Ion ◽  
Surface Functional Groups ◽  
Carbon Nanosheets ◽  
Hierarchically Porous ◽  
Li Ion ◽  
Unique Surface

High performance carbon-based materials are ideal electrode materials for Li-ion capacitors (LICs), but there are still many challenges such as the complicated preparation preocesses, high cost and low yield. Also,...

Synthesis and Electrochemical Properties of Spherical LiFePO4/C Composite via Spray Drying

2015 ◽  
Vol 1120-1121 ◽  
pp. 554-558 ◽  
Author(s):  
Juan Mei Wang ◽  
Bing Ren ◽  
Ying Lin Yan ◽  
Qing Zhang ◽  
Yan Wang
Keyword(s):  
Diffusion Coefficient ◽  
Electrochemical Properties ◽  
Spray Drying ◽  
Electrochemical Impedance ◽  
Retention Rate ◽  
Lithium Ion ◽  
Constant Current ◽  
Ion Diffusion ◽  
Electrochemical Tests ◽  
Li Ion

In this work, spherical LiFePO4/C composite had been synthesized by co-precipitation and spray drying method. The structure, morphology and electrochemical properties of the samples were characterized by X-ray diffraction (XRD), scanning electron micrograph (SEM), transmission electron microscope (TEM), constant current charge-discharge tests and electrochemical impedance spectroscopy (EIS) tests. The spherical LiFePO4/C particles consisted of a number of smaller grains. The results showed that the morphology of LiFePO4/C particles seriously affected the Li-ion diffusion coefficient and electrochemical properties of lithium ion batteries. Electrochemical tests revealed the spherical LiFePO4/C composite had excellent Li-ion diffusion coefficient which was calculated to be 1.065×10-11 cm2/s and discharge capacity of 149 (0.1 C), 139 (0.2 C), 133 (0.5 C), 129 (1 C) and 124 mAhg-1(2 C). After 50 cycles, the capacity retention rate was still 93.5%.

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.

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