Fault Diagnosis of Lithium Battery based on Fuzzy Bayesian Network

Li, Ran

doi:10.23940/ijpe.18.10.p6.23022311

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…With electrical current, voltage and granulation time as inputs, SOC prediction model was built to predict SOC. In literature [11], a dual filter comprising standard Kalman filter and unscented Kalman filter was developed for the first time to incorporate with SVR for predicting SOC. Prediction of SOC using SVR in above methods achieved effective fitting and improved prediction accuracy.…”

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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“…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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Fault Diagnosis of Lithium Battery based on Fuzzy Bayesian Network

Cited by 2 publications

References 9 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 of Zinc-Silver Battery: A Classification Strategy Based on PSO-LSSVM

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