Fractional‐order modeling and SOC estimation of lithium‐ion battery considering capacity loss

Summary For state‐of‐charge (SOC) estimation, the resistance deterioration and continuous capacity loss can lead to erroneous estimation results. In this paper, an SOC estimator of lithium‐ion battery based on the fractional‐order model and adaptive dual Kalman filtering algorithm is proposed first....

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Bibliographic Details
Published inInternational journal of energy research Vol. 43; no. 1; pp. 417 - 429
Main Authors Li, Shuxian, Hu, Minghui, Li, Yunxiao, Gong, Changchao
Format Journal Article
LanguageEnglish
Published Bognor Regis Hindawi Limited 01.01.2019
Subjects
Accuracy
Adaptive algorithms
Adaptive filters
Batteries
Capacity
capacity estimation
Chemical industry
Computer simulation
Filtration
fractional‐order model
Insurance claims
Kalman filters
Lithium
lithium‐ion battery
Modelling
Price increases
State of charge
state‐of‐charge estimation
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Summary:Summary For state‐of‐charge (SOC) estimation, the resistance deterioration and continuous capacity loss can lead to erroneous estimation results. In this paper, an SOC estimator of lithium‐ion battery based on the fractional‐order model and adaptive dual Kalman filtering algorithm is proposed first. Then, to improve the accuracy of SOC estimation considering capacity loss, the particle filter algorithm is applied to update capacity online in real time. Then, an SOC estimation method is proposed considering battery capacity loss. The simulation results show that the accuracy of battery capacity prediction based on particle filter is high under the condition of capacity loss.
ISSN:0363-907X
1099-114X
DOI:10.1002/er.4275