A Rayleigh Quotient-Based Recursive Total-Least-Squares Online Maximum Capacity Estimation for Lithium-Ion Batteries


The utmost capacity, the quantity of maximal electric charge that a battery can store, not solely indicates the state of health, but conjointly is required in various strategies for state-of-charge estimation. This paper proposes an alternate approach to perform on-line estimation of the most capacity by solving the recursive total-least-squares (RTLS) problem. Totally different from previous art, the proposed approach poses and solves the RTLS as a Rayleigh quotient optimization problem. The Rayleigh quotient-based mostly approach will be readily generalized to different parameter estimation problems as well as impedance estimation. Compared with other capacity estimation strategies, the proposed algorithm enjoys the benefits of existing RTLS-based algorithms for instance, low computation, straightforward implementation, and high accuracy, and so is suitable for use in real-time embedded battery management systems. The proposed method is compared with existing ways via simulations and experiments.

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