A robust approach to battery fuel gauging, part II: Real time capacity estimation

In this paper, the second of a series on battery fuel gauging, we present an approach for real time capacity estimation. In part I of this series, we presented a real time parameter estimation approach for various battery equivalent models. The proposed capacity estimation scheme has the following novel features: it employes total least squares (TLS) estimation in order to account for uncertainties in both model and the observations in capacity estimation. The TLS method can adaptively track changes in battery capacity. We propose a second approach to estimate battery capacity by exploiting rest states in the battery. This approach is devised to minimize the effect of hysteresis in capacity estimation. Finally, we propose a novel approach for optimally fusing capacity estimates obtained through different methods. Then, the proposed algorithm was validated using hardware-in-the-loop (HIL) data collected from commercially available Li-ion batteries. The proposed approach performs within 1% or better accuracy in terms of capacity estimation based on both simulated as well as HIL evaluations.