Online simultaneous identification of parameters and order of a fractional order battery model

Fractional order models have been successfully applied to estimate states and diagnose faults for lithium ion batteries. However, their order has not been identified online, which restricts their applications in battery management systems due to the intuitive nonlinearity of fractional order identification. In this study, a novel online method is proposed to identify the parameters and order of a fractional order model for lithium ion batteries using least squares and a gradient-based method, respectively. This online method is validated against both simulation and experimental results. Compared with the fixed-order method under different operation conditions, the proposed method has achieved better model accuracy and robustness of identified model parameters. Furthermore, a hardware-in-the-loop test is also used to verify the efficacy of the proposed method. Based on the analysis of the online identification results, the limitations of existing fractional order models are also pointed out, and the directions to further improve the existing models are discussed.