A Rayleigh Quotient-Based Recursive Total-Least-Square Online Maximum Capacity Estimation for Lithium-ion Batteries

The maximum capacity, the amount of maximal electric charge that a battery can store, not only indicates the state of health, but also is assumed in numerous methods for state of charge estimation. This paper proposes an alternative approach to perform the online estimation of the maximum capacity by solving the recursive total least square (RTLS) problem. Different from prior art, the proposed approach poses and solves the RTLS problem as a Rayleigh quotient optimization problem. The Rayleigh quotient-based approach can be readily generalized to other parameter estimation problems including impedance estimation. Compared to other capacity estimation methods, the proposed algorithm enjoys the advantages of other existing RTLS-based algorithms for instance, low computational cost, simple implementation, and high accuracy. The proposed method is compared with existing methods via simulations and
experiments. The proposed method is suitable for use in real-time embedded battery management systems.