Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery

Physics-based models for lithium-ion battery have been regarded as a promising alternative to equivalent circuit models due to their ability to describe internal electrochemical states of battery. However, the huge computational burden and numerous parameters of these models impede their application in embedded battery management system. To deal with the above problem, a reduced-order physics-based model for lithium-ion battery with better tradeoff between the model fidelity and computational complexity is developed. A strategy is proposed to extend the operation from a fixed point to full state of charge range. As the model consists of constant, varying, identifiable and unidentifiable parameters, it is impractical to identify the full set of parameters only using the current-voltage data. To sort out the identifiable parameters, a criterion based on calculating the determinant and condition number of Fisher information matrix (FIM) is employed. A subset with maximum nine identifiable parameters is obtained and then identified by nonlinear least square regression algorithm with confidence region calculated by FIM. Compared with the outputs from commercial software, the effectiveness of the battery model and extending strategy are verified. The estimated parameters deviate from the true values slightly, and produce small voltage errors at different current profiles.