Reliability assessment and failure analysis of lithium iron phosphate batteries

Ran, Li; Wu, Jian; Wang, Haiying; Jian-ying, Guo; Li, Ge-Chen

doi:10.1016/j.ins.2013.06.038

Cited by 32 publications

(9 citation statements)

References 21 publications

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“…In the meantime, given the difficulty in obtaining data of vehicle lithium-ion battery under some extreme conditions, the generalization ability of SVR model under PSO constraint is impacted by the difficulty to ensure sufficient sample size. Whether with good generalization ability or not is a critical indicator to measure the performance of support vector regression [12], [13]. In this study, the performances of the model are evaluated and compared by introducing cross-validation method; thus, the model can exhibit good generalization ability [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation

et al. 2020

IEEE Access

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Lithium-ion battery refers to a complex nonlinear system. Real-time diagnosis and accurate prediction of battery state of charge(SOC) parameters are hotspots and critical issues in battery research. To reduce the dependence of state of charge prediction on battery model accuracy and speed, and achieve real-time online estimation, a SOC prediction model of lithium-ion battery system is developed based on the model of support vector machine (SVM). SVM parameter is optimized using an algorithm of particle swarm optimization, and the performance of prediction model is assessed using cross-validation. The obtained experimental data is simulated, involving the comparison with the support vector machine model, and the prediction simulation of the battery in the state of fault. The results reveal that this model with a better performance than that of the support vector machine exhibits high accuracy and generalization ability.

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Section: Introductionmentioning

confidence: 99%

State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation

et al. 2020

IEEE Access

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“…Regarding battery sorting, all of these methods can be improved and utilized. For example, Figure 13 presents a linear least squares estimation diagram of the parameters of lithium iron phosphate battery samples [142]. It may be difficult to optimize the statistical algorithm in the future.…”

Section: Methodsmentioning

confidence: 99%

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Zhang

et al. 2020

WEVJ

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To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect.

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“…Battery samples 1∼50 were trained, and the projection of the trained LS-SVM on two-dimensional plane is shown in Figure 6. It could be seen from the figure that the trained LS-SVM sorter had distinct boundaries and clear class, suggesting that battery classification model built by binary tree support vector machine can easily implement battery sorting and guarantee consistency after sorting batteries in groups [34], [35].…”

Section: Model Trainingmentioning

confidence: 99%

Toward Group Applications of Zinc-Silver Battery: A Classification Strategy Based on PSO-LSSVM

Zhou

et al. 2020

IEEE Access

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To solve problem of the reliability and consistency of silver-zinc batteries after being sorted into groups, a proposed classification strategy of zinc-silver battery based on least squares support vector machine with PSO (PSO-LSSVM) was proposed in this paper. Sample data was extracted from the charging curve of silver-zinc batteries to pre-sort training samples using FCM clustering. The least squares support vector machine model parameters were optimized and improved using particle swarm optimization algorithm. The method breaks the limitation of building battery classification model based on prior knowledge, reduces the dependence on parameter selection, and enhances model training speed and accuracy. In the end, experimental data was used for battery classification model training and testing. Test results show that the battery pack obtained by the group strategy has good dynamic consistency, the rate of capacity decay is significantly reduced. The rate of capacity decay is no more than 10% after 30 cycles of life test. The silver-zinc battery group classification strategy proposed to this paper improves the consistency and reliability of the battery and the life of battery packs.

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Reliability assessment and failure analysis of lithium iron phosphate batteries

Cited by 32 publications

References 21 publications

State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation

State of Charge Prediction Algorithm of Lithium-Ion Battery Based on PSO-SVR Cross Validation

Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries

Toward Group Applications of Zinc-Silver Battery: A Classification Strategy Based on PSO-LSSVM

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