Analysis of the Deposit Layer from Electrolyte Side Reaction on the Anode of the Pouch Type Lithium Ion Polymer Batteries: The Effect of State of Charge and Charge Rate

2014 ◽  
Vol 149 ◽  
pp. 1-10 ◽  
Author(s):  
Victor A. Agubra ◽  
Jeffrey W. Fergus ◽  
Rujian Fu ◽  
Song-yul Choe
Keyword(s):  
Lithium Ion ◽  
Side Reaction ◽  
State Of Charge ◽  
Charge Rate ◽  
Deposit Layer

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

scholarly journals Performance Analysis of Indentation Punch on High Energy Lithium Pouch Cells and Simulated Model Improvement

Polymers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1971
Author(s):  
Lihua Ye ◽  
Muhammad Muzamal Ashfaq ◽  
Aiping Shi ◽  
Syyed Adnan Raheel Shah ◽  
Yefan Shi
Keyword(s):  
Short Circuit ◽  
Lithium Ion ◽  
Mechanical Deformation ◽  
High Energy ◽  
State Of Charge ◽  
Punch Test ◽  
Short Course ◽  
Model Cell ◽  
The Impact

In this research, the aim relates to the material characterization of high-energy lithium-ion pouch cells. The development of appropriate model cell behavior is intended to simulate two scenarios: the first is mechanical deformation during a crash and the second is an internal short circuit in lithium-ion cells during the actual effect scenarios. The punch test has been used as a benchmark to analyze the effects of different state of charge conditions on high-energy lithium-ion battery cells. This article explores the impact of three separate factors on the outcomes of mechanical punch indentation experiments. The first parameter analyzed was the degree of prediction brought about by experiments on high-energy cells with two different states of charge (greater and lesser), with four different sizes of indentation punch, from the cell’s reaction during the indentation effects on electrolyte. Second, the results of the loading position, middle versus side, are measured at quasi-static speeds. The third parameter was the effect on an electrolyte with a different state of charge. The repeatability of the experiments on punch loading was the last test function analyzed. The test results of a greater than 10% state of charge and less than 10% state of charge were compared to further refine and validate this modeling method. The different loading scenarios analyzed in this study also showed great predictability in the load-displacement reaction and the onset short circuit. A theoretical model of the cell was modified for use in comprehensive mechanical deformation. The overall conclusion found that the loading initiating the cell’s electrical short circuit is not instantaneously instigated and it is subsequently used to process the development of a precise and practical computational model that will reduce the chances of the internal short course during the crash.

scholarly journals Improvement of long-term cycling performance of high-nickel cathode materials by ZnO coating

2021 ◽  
Vol 10 (1) ◽  
pp. 210-220
Author(s):  
Fangfang Wang ◽  
Ruoyu Hong ◽  
Xuesong Lu ◽  
Huiyong Liu ◽  
Yuan Zhu ◽  
...  
Keyword(s):  
Electrochemical Performance ◽  
Lithium Ion Batteries ◽  
Solid Phase ◽  
Low Cost ◽  
Specific Capacity ◽  
High Capacity ◽  
Lithium Ion ◽  
Side Reaction ◽  
Chemical Route ◽  
High Nickel

Abstract The high-nickel cathode material of LiNi0.8Co0.15Al0.05O2 (LNCA) has a prospective application for lithium-ion batteries due to the high capacity and low cost. However, the side reaction between the electrolyte and the electrode seriously affects the cycling stability of lithium-ion batteries. In this work, Ni2+ preoxidation and the optimization of calcination temperature were carried out to reduce the cation mixing of LNCA, and solid-phase Al-doping improved the uniformity of element distribution and the orderliness of the layered structure. In addition, the surface of LNCA was homogeneously modified with ZnO coating by a facile wet-chemical route. Compared to the pristine LNCA, the optimized ZnO-coated LNCA showed excellent electrochemical performance with the first discharge-specific capacity of 187.5 mA h g−1, and the capacity retention of 91.3% at 0.2C after 100 cycles. The experiment demonstrated that the improved electrochemical performance of ZnO-coated LNCA is assigned to the surface coating of ZnO which protects LNCA from being corroded by the electrolyte during cycling.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

Sign in / Sign up

Export Citation Format

Share Document