Robust battery fuel gauge algorithm development, part 1: Online parameter estimation

In this paper, we present a novel voltage drop model for battery SOC tracking and develop a robust, realtime approach for model parameter estimation. The proposed model avoids the need to model hysteresis voltage that hard to model and estimate in practical applications. Another advantage of the proposed voltage drop model is that the parameters of the model is estimated linearly, regardless of the model complexity, i.e., number of RC elements considered in the model. We identify the presence of correlated noise that has been so far ignored in the literature and use it to enhance the accuracy of model identification. The proposed parameter approach enables a robust initialization/re-initialization strategy for continuous operation of the battery fuel gauge (BFG). The performance of the online parameter estimation scheme was evaluated through objective measures.