Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management
Joint Authors
Li, Kang
Guo, Yuanjun
Liu, Kailong
Peng, Qiao
Zhang, Li
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-05
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Temperature is a crucial state to guarantee the reliability and safety of a battery during operation.
The ability to estimate battery temperature, especially the internal temperature, is of paramount importance to the battery management system for monitoring and thermal control purposes.
In this paper, a data-driven approach combining the RBF neural network (NN) and the extended Kalman filter (EKF) is proposed to estimate the internal temperature for lithium-ion battery thermal management.
To be specific, the suitable input terms and the number of hidden nodes for the RBF NN are first optimized by a two-stage stepwise identification algorithm (TSIA).
Then, the teaching-learning-based optimization (TLBO) algorithm is developed to optimize the centres and widths in every neuron of basis function.
After optimizing the RBF NN model, a battery lumped thermal model is adopted as the state function with the EKF to filter out the outliers of the RBF model and reduce the estimation error.
This data-driven approach is validated under four different conditions in comparison with the linear NN models.
The experimental results demonstrate that the proposed RBF data-driven approach outperforms the other approaches and can be extended to other types of batteries for thermal monitoring and management.
American Psychological Association (APA)
Liu, Kailong& Li, Kang& Peng, Qiao& Guo, Yuanjun& Zhang, Li. 2018. Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management. Complexity،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1136834
Modern Language Association (MLA)
Liu, Kailong…[et al.]. Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management. Complexity No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1136834
American Medical Association (AMA)
Liu, Kailong& Li, Kang& Peng, Qiao& Guo, Yuanjun& Zhang, Li. Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management. Complexity. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1136834
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136834