A Non-dimensional Input Excitation Optimization Approach for Battery Health

Huang, Rui and Fogelquist, Jackson and Kuang, Simon and Lin, Xinfan (2023) A Non-dimensional Input Excitation Optimization Approach for Battery Health. IFAC-PapersOnLine, 56 (3). pp. 157-162.

Text (Jurnal Teknik Industri) A-Non-dimensional-Input-Excitation-Optimization-Approach-for_2023_IFAC-Paper.pdf - Published Version Download (481kB)

Abstract

Model parameter estimation is an important subject in control engineering, including the field of battery management. Input excitation optimization has become an emerging topic lately to improve the accuracy of estimation. Traditional optimization approach suffers from a fundamental issue in parameter uncertainty, as the target parameters for estimation, often needed for computing the optimization objective and constraints, are intrinsically unknown. In this study, we introduce a non-dimensional approach to optimize excitations for estimating the health-related Li-ion battery electrochemical parameters. Guided by the Buckingham π theorem, we derived a control-oriented non-dimensional battery model, excluding uncertain target parameters from the problem formulation. As a result, the optimization problem can be solved without any prior knowledge of target parameters. The applicable control input sequence can be recovered by rescaling the obtained non-dimensional sequence with the best available knowledge of the parameters. Furthermore, the proposed method reveals the fundamental impact of the unknown parameters on the solution of input optimization. In light of this finding, we propose two iterative excitation optimization strategies, which both significantly improve the robustness and reduce the complexity of the optimization problem. The proposed method can be generalized to solve general optimal control problems for a broad class of systems.

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