scholarly journals Research on the SOH Prediction Based on the Feature Points of Incremental Capacity Curve

2021 ◽  
Author(s):  
Qian Zhao ◽  
Haobin Jiang ◽  
Biao Chen ◽  
Cheng Wang ◽  
Lv Chang
Keyword(s):  
Lithium Ion ◽  
Principal Component ◽  
Maximum Error ◽  
Normal Operation ◽  
Feature Points ◽  
Fitting Method ◽  
Voltage Curve ◽  
Location Parameters ◽  
Incremental Capacity ◽  
Location Change

Abstract The accurate prediction of the state of health (SOH) is an important basis for ensuring the normal operation of the lithium-ion battery (LIB). The accurate SOH can extend the life-span, ensure safety, and improve the performance of LIBs. The charging voltage curve and incremental capacity (IC) curve of the LIB in different SOH are obtained through experiments. The location parameters of each feature point on IC curve are closely related to battery aging, to characterize the SOH of the LIB with the location of feature points. To solve the difficulty in identifying feature points due to the oscillation in solving IC curves with a traditional numerical analytic method, the piecewise polynomial fitting method is adopted to smooth IC. To discuss the law between the location change of all feature points on the IC curve and the capacity attenuation, a capacity prediction regression model is established after the dimensionality reduction of the coordinate data of feature points on the IC curve with the principal component analysis method. The proposed method can rapidly estimate the online SOH of LIBs during the charging process of electric vehicles and the results show the maximum error is 0.63AH (3.15%).

scholarly journals Development of a voltage curve prediction model for lithium-ion battery based on destructive tests

2021 ◽  
Vol 1826 (1) ◽  
pp. 012091
Author(s):  
R. S. D. Teixeira ◽  
D. R. Louzada ◽  
L. A.P. Gusmão ◽  
R. F. Calili
Keyword(s):  
Prediction Model ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Voltage Curve

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

Screening of retired lithium-ion batteries using incremental capacity charging curve based residual capacity estimation method for facilitating sustainable circular lithium-ion battery system

2021 ◽  
pp. 1-16
Author(s):  
Honglei Li ◽  
Liang Cong ◽  
Huazheng Ma ◽  
Weiwei Liu ◽  
Yelin Deng ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Capacity Fade ◽  
Characteristic Parameters ◽  
Capacity Estimation ◽  
Capacity Curve ◽  
Residual Capacity ◽  
Remaining Capacity ◽  
Incremental Capacity

Abstract The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. The main objective of this paper is to develop a fast and accurate capacity estimation method to classify the retired batteries by the remaining capacity. The hybrid technique of adaptive genetic algorithm and back propagation neural network is developed to estimate battery remaining capacity using the training set comprised of the selected characteristic parameters of incremental capacity curve of battery charging. Also, the paper investigated the correlation between characteristic parameters with capacity fade. The results show that capacity estimation errors of the proposed neural network are within 3%. Peak intensity of the incremental capacity curve has strong correlation with capacity fade. The findings also show that the translation of peak of the incremental capacity curve is strongly related with internal resistance.

scholarly journals Analysis of Excess Consumption in College Students by KPAG Method During the COVID-19 Pandemic

2021 ◽  
Vol 2 (1) ◽  
pp. 1-3
Author(s):  
Bin Zhao ◽  
◽  
Jinming Cao ◽  
Keyword(s):  
College Students ◽  
Early Warning ◽  
Principal Component ◽  
Real Data ◽  
Kernel Principal Component Analysis ◽  
Polynomial Kernel ◽  
Consumption Behavior ◽  
Fitting Method ◽  
Data Dimensionality Reduction ◽  
Excessive Consumption

With the arrival of COVID-19, some areas are under closed management, bringing about changes in the way people consume. It also leads to the excessive consumption of some people, especially college students. In order to give early warning to unreasonable consumption behavior, this study designed KPAG algorithm to give early warning to consumption risk. Using particle swarm optimization (PSO) kernel principal component analysis (KPCA) parameter optimization, optimal polynomial kernel to delete data information, and ant colony genetic algorithm (association) clustering analysis of data dimensionality reduction, according to the consumption behavior of college students are divided into three categories, for the consumption behavior of college students to build an early warning model. Through the classification and verification experiment of real data, the results show that compared with the traditional PCA data fitting method, the accuracy of the model in this paper can reach 90%, which is more reliable than the traditional algorithm, and the accuracy of the model is improved by nearly 20%, which can be used for effective early warning.

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