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An analytical model for predicting the remaining battery capacity of lithium-ion batteries

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2 Author(s)
Peng Rong ; Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA ; M. Pedram

Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing and applying an effective dynamic power management policy for the device. This paper starts up by showing that a 30% error in predicting the battery capacity of a lithium-ion battery can result in up to 20% performance degradation for a dynamic voltage and frequency scaling algorithm. Next, this paper presents a closed form analytical expression for predicting the remaining capacity of a lithium-ion battery. The proposed high-level model, which relies on online current and voltage measurements, correctly accounts for the temperature and cycle aging effects. The accuracy of the high-level model is validated by comparing it with DUALFOIL simulation results, demonstrating a maximum of 5% error between simulated and predicted data.

Published in:

IEEE Transactions on Very Large Scale Integration (VLSI) Systems  (Volume:14 ,  Issue: 5 )