Adaptive Parameter Identification of Battery Pack inElectric Vehicles with Real-Driving Signals
This paper presents an adaptive identification method for battery parameters in automotive applications. A simple yet accurate electrical equivalent model (ECM) with varying parameters is used to represent the whole battery pack. The modeling process requires the current, voltage, and SOC sign...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-03-01
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| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30809 |
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| Summary: | This paper presents an adaptive identification method
for battery parameters in automotive applications. A simple
yet accurate electrical equivalent model (ECM) with varying
parameters is used to represent the whole battery pack. The
modeling process requires the current, voltage, and SOC
signals of the battery. Detailed physical knowledge of the
battery pack and inside cells are not necessary. The ECM
parameter identification approach is developed by employing
the NLMS (normalized least mean square) algorithm, which is
an advanced adaptive algorithm having fast convergence rate
and easier to be implemented. This approach is verified on
a 51.2 V, 95AH LiFePO4 battery pack operated in
three-wheeler electric bikes. Battery signals during
vehicle daily real-world driving were collected over a
period of time and used for the ECM parameter
identification. The identified internal resistance R0, R1
and capacitance C1 changes obviously over the period of
time and the battery degradation is well reflected through
the identified parameters of the ECM.
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| ISSN: | 1435-4934 |