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

TLDR

Predicting the residual energy of the battery powering a portable device is essential for designing effective dynamic power‑management policies. This study introduces a closed‑form analytical expression to predict the remaining capacity of a lithium‑ion battery. The model derives capacity estimates from online current and voltage measurements while accounting for temperature and cycle‑aging effects. The model achieves a maximum 5% prediction error, and a 30% error can cause up to 20% performance degradation in dynamic voltage‑frequency scaling algorithms.

Abstract

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.