Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

Tang, Xiaopeng; Liu, Kailong; Wang, Xin; Liu, Boyang; Gao, Furong; Widanage, Widanalage Dhammika

doi:10.1016/j.jpowsour.2019.227118

Cited by 58 publications

(30 citation statements)

References 52 publications

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“…Considering the correlation between aging speed and internal chemistry, it is reasonable that cells with similar chemical/physical states at certain time point will experience different aging speeds under the same environmental stress (such as electric stress, temperature stress, mechanical stress) and present varied chemical/physical states in the following time points 18,42,43 . In this study, cell degradation under room temperature storage was mainly caused by the SEI film formation side reaction, which is influenced by the surface area of the anode active material, the thickness of the anode and the original SEI film formed during the preparation process 44,45 . Therefore, although the cells with similar chemical/physical states were degraded under the same operation conditions, they will still experience different aging speeds over a long time period.…”

Section: Comparison Of Cis and State-of-art Test For This Test 80 Cmentioning

confidence: 97%

A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries

Xie

Ren

Wang

et al. 2020

Sci Rep

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over the past decade, it has been repeatedly demonstrated that homogeneity in electrochemical performance of lithium-ion cells plays a major role in determining the life and safety of lithium-ion battery modules or packs. Generally, the homogeneity of a battery pack is evaluated by characterizing the cells individually in terms of capacity, mass, impedance. particularly, high quality electrochemical data heavily relies on the availability of high precision current source to minimize the discrepancy induced by the channel-to-channel variation. Here, a facile and precise measurement method is reported for screening cell-to-cell variations, in which voltage is the only indicator parameter independent of high precision current source. in detail, by connecting the cells in series (ciS), the measurement error of electrochemical data caused by stability and discrepancy of current sources among different charge/discharge equipment can be effectively avoided. The findings of this work showed that the cell-to-cell variations can be simply and sensitively detected with CiS configuration. for example, the relative standard deviation, which is the evaluation criterion of battery homogeneity, was 2.14% based on CiS while it was 0.43% based on individual measurements. The simple and precise ciS measurement is promising for evaluation of cell quality or module integration quality. in addition, this work can also provide a solid foundation for the development of detection algorithms for battery management systems to rapidly monitor battery homogeneity.Lithium ion batteries (LIBs) have to be integrated into modules and packs for large-scale applications such as electric vehicles (EVs) and stationary energy storage systems 1-7 . However, a reliable and long-lasting power system is determined mostly by the cell homogeneity rather than the performance of individual cells 8,9 . The cell-to-cell variations in terms of capacity and impedance will subject individual cells to different levels of state of charge (SOC), current, temperature and aging, which in turn would accelerate the degradation of electrochemical performance or even lead to safety performance of the power system 2,10-13 . For instance, a weak cell with a smaller capacity than the average in a series-connected module will be repeatedly overcharged (over-discharged) during charging (discharging) process if the charge/discharge cutoff condition is determined according to the total voltage 14 . The weaker cell will then have faster decay than others and will be susceptible to failure under harsh conditions. The available energy of the module will also be limited by the weakest cell 15 . Therefore, there is an urgent need to understand and minimize the cell-to-cell variations in a battery module.There are different factors leading to variations among cells 11 , including manufacturing factors (manufacturing tolerances, quality control and process design), and environmental factors such as temperature gradient within the battery pack 16 . Rumpf et al. 12 has evaluated the cel...

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Section: Comparison Of Cis and State-of-art Test For This Test 80 Cmentioning

confidence: 97%

A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries

Xie

Ren

Wang

et al. 2020

Sci Rep

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“…In Algorithm 1, several model forms such as single exponential function [21], dual exponential function [22], power function [23], hybrid linear function [24], and polynomial function [25] are generally used for f (•), there also exists lots of algorithms to identify these models' parameters. Among these methods, MATLAB nonlinear curve fitting algorithm has been packed into the standard toolbox and widely used in different areas.…”

Section: A Conventional Empirical Model-based Predictionmentioning

confidence: 99%

“…Rather than using complex electrochemical models, empirical model based methods portray the battery capacity degradation over time with mathematic functions such as single exponential function [21], dual exponential function [22], power function [23], hybrid linear function [24], and polynomial function [25]. Then different data-fitting techniques such as particle filters [22], Kalman filters [26] or some offline optimization algorithms [27,28] are employed to identify the models' parameters.…”

mentioning

confidence: 99%

“…Various initialization methods would lead to different network training results [38]. In this case, even if the same dataset is used to train NN, various aging trajectories could be generated, resulting in the increase of users' anxiety on battery lifespan [23]. However, as a pure data-driven method, how to select a suitable initialization solution for battery aging trajectory prediction is still an open issue [20].…”

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confidence: 99%

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Model Migration Neural Network for Predicting Battery Aging Trajectories

Tang

Liu

Wang

et al. 2020

IEEE Trans. Transp. Electrific.

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132

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“…Due to the uncertain aging effect of lithium battery, the RUL prediction of lithium battery has a great challenge [64][65][66][67][68]. The RUL prediction approaches of lithium battery are mainly divided into three types: particle filter, artificial intelligence and stochastic process modeling.…”

Section: B a Practical Case Studymentioning

confidence: 99%

Remaining Useful Life Prediction Under Imperfect Prior Degradation Information

et al. 2020

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The remaining useful life (RUL) prediction is the core of equipment maintenance and decisionmaking. Accurate RUL prediction can make effective maintenance before the failure occurs to reduce the probability of equipment failure. However, in industrial practice, we often face the situation that prior information is insufficient or inaccurate, which could influence our prediction of RUL and reduce the prediction accuracy severely. To solve this problem, we study the issue of RUL prediction under imperfect prior degradation data by reasonably fusing the prior information and the field degradation data. Firstly, based on the linear Wiener degradation process, we prove a conclusion that the random parameter estimation results by the joint Bayesian algorithm and the expectation-maximization (EM) algorithm are the same as that by the maximum likelihood estimation (MLE) method. It shows that the joint estimation method completely overcomes the influence of prior information, and the more iterations, the smaller proportion of prior information. Secondly, we also prove that the random parameter joint updating method for the nonlinear Wiener degradation process has the same characteristics. Then, a heuristic algorithm that reasonably fuses the prior information and the field degradation data is proposed, which controls the number of iterations of the joint updating algorithm based on the credibility of the prior information. It is also applied to the nonlinear degradation process by not iterating the nonlinear coefficients. Finally, the correctness of the conclusion of this paper is verified by the simulated degradation data, and the effectiveness of the proposed heuristic updating method is verified by the actual lithium battery degradation data.

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Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 58 publications

References 52 publications

A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries

A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries

Model Migration Neural Network for Predicting Battery Aging Trajectories

Remaining Useful Life Prediction Under Imperfect Prior Degradation Information

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