Liu, K, Li, K orcid.org/0000-0001-6657-0522 and Deng, J (2016) A novel hybrid data-driven method for li-ion battery internal temperature estimation. In: Proceedings of the 2016 UKACC 11th International Conference on Control (CONTROL). 2016 UKACC 11th International Conference on Control (CONTROL), 31 Aug - 02 Sep 2016, Belfast, UK. IEEE ISBN 978-1-4673-9891-6
Abstract
Accurate battery internal temperature estimation is a key for safe battery operation of electric vehicles. In this paper, a novel hybrid data-driven method combining a linear neural network (NN) model and an extended Kalman filter (EKF) is developed to estimate the internal temperature of a LiFePo4 battery. In order to select the proper input terms of the linear NN model and estimate the associated parameters, a fast recursive algorithm (FRA) is firstly used. Then an EKF with a battery lumped thermal model as the state function is used to filter out the outliers and reduce the errors in estimating the internal temperature based on the linear NN model. The test results from two different experiment data demonstrate that the hybrid method can achieve good estimation accuracy, and the method can be easily applied to other type of batteries.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Battery internal temperature estimation; Linear neural network; Fast recursive algorithm; Extended Kalman filter; Lumped thermal model |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Nov 2019 14:13 |
Last Modified: | 05 Nov 2019 14:13 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/control.2016.7737560 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153038 |