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State-of-Charge and State-of-Health prediction of Lead-acid Batteries for Hybrid Electric Vehicles using non-linear observers
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| Author(s) |
BENTLEY Paul; BINGHAM Chris; BHANGU Bikramjit; STONE Dave |
| Abstract |
The paper describes the application of state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Approaches based on the Extended Kalman Filter (EKF) are presented to provide correction for offset, drift and state divergence—an unfortunate feature of more traditional coulomb-counting techniques. Experimental results are employed to demonstrate the relative attributes of the proposed methodology. |
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| Filename: | 0654 |
| Filesize: | 212.4 KB |
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| Type |
Members Only |
| Date |
Last modified 2006-02-05 by System |
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