Abstract
Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM) and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters) and a nonparametric part [involving the open-circuit voltage (OCV)]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA) expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.
Highlights
Lithium-ion batteries play a significant role in the energy storage devices used in electric vehicles (EVs) because of their high energy density, low self-discharge rate, lack of a memory effect, high operating voltage, and long life cycle
To overcome the above-mentioned disadvantages, this paper presents a semiparametric battery model based on the Thevenin equivalent circuit model, which is a compromise between the nonparametric and parametric battery models
To characterize the dynamic behavior of lithium-ion batteries for EVs, this paper has proposed a novel semiparametric identification approach based on the wavelet-based partially linear battery model (PLBM) model
Summary
Lithium-ion batteries play a significant role in the energy storage devices used in electric vehicles (EVs) because of their high energy density, low self-discharge rate, lack of a memory effect, high operating voltage, and long life cycle. The primary concerns in the design of EVs are how to maintain optimum battery performance and extend the battery’s expected life To achieve these goals, the battery must have a well-designed battery management system (BMS). The BMS must track the dynamic behaviors of the battery for reliable and efficient operation, which requires battery models that can accurately determine battery behavior under various operating conditions. Parametric battery models have been developed in terms of the equivalent electric-circuit parameters (for electrical models) or electrochemical parameters (for electrochemical models). These parameters have clear physical meaning and can be used to investigate the working status of batteries. Electrical model parameters, such as the open-circuit voltage (OCV)
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