Enhanced Identification Algorithms for Battery Models under Noisy Measurements

This paper aims to develop some enhanced identification algorithms for real time characterization of battery dynamics. The core of such a system is advanced system identification techniques that provide fast tracking capability to update battery cell's individual models in real-time operation. Due to inevitable measurement noises on voltage and current observations, the identification algorithms must perform under both input and output noises, leading to more challenging issues than standard identification problems. It is shown that typical battery models may not be identifiable, unique battery model features require modified input/output expressions, and standard least-squares identification methods will encounter identification bias. This paper devises modified model structure and algorithms to resolve these issues. System identifiabihty, algorithm convergence, identification bias, and bias correction mechanisms are established. Typical battery model structures are used to illustrate utilities of the methods.