Abstract
State-of-charge (SOC) is one of the most critical parameters in battery management systems (BMSs). SOC is defined as the percentage of the remaining charge inside a battery to the full charge, and thus ranges from 0% to 100%. This percentage value provides important information to manufacturers about the performance of the battery and can help end-users identify when the battery must be recharged. Inaccurate estimation of the battery SOC may cause over-charge or over-discharge events with significant implications for system safety and reliability. Therefore, it is crucial to develop methods for improving the estimation accuracy of battery SOC. This paper presents an electrochemical model for lithium-ion battery SOC estimation involving the battery’s internal physical and chemical properties such as lithium concentrations. To solve the computationally complex solid-phase diffusion partial differential equations (PDEs) in the model, an efficient method based on projection with optimized basis functions is presented. Then, a novel moving-window filtering (MWF) algorithm is developed to improve the convergence rate of the state filters. The results show that the developed electrochemical model generates 20 times fewer equations compared with finite difference-based methods without losing accuracy. In addition, the proposed projection-based solution method is three times more efficient than the conventional state filtering methods such as Kalman filter.
Highlights
The size of the market for lithium-ion (Li-ion) batteries has grown considerably in recent years
The results show that the developed electrochemical model generates 20 times fewer equations compared with finite difference-based methods without losing accuracy
The optimized basis function is constructed in three steps: (i) definition of elementary basis functions, e.g., even-order monomials, (ii) orthonormalization of elementary basis function to ensure the numerical stability of the generated equations, and (iii) linear transformation of the orthonormal basis function
Summary
The size of the market for lithium-ion (Li-ion) batteries has grown considerably in recent years. Physics-based methods have been applied to Li-ion battery SOC estimation [6,7]. These methods have intrinsic advantages over traditional Coulomb counting and voltage-based methods. State filters are required when using a physics-based model for battery SOC estimation. The current study aims to develop a physics-based electrochemical model for Li-ion battery.
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